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1
Mo, Wenting, Sheng-Uei Guan, and Sadasivan Puthusserypady. "Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms." International Journal of Applied Evolutionary Computation 1, no. 2 (April 2010): 1–27. http://dx.doi.org/10.4018/jaec.2010040101.
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Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.
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di Pierro, F., S. Djordjević, Z. Kapelan, S. T. Khu, D. Savić, and G. A. Walters. "Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm." Water Science and Technology 52, no. 5 (September 1, 2005): 43–52. http://dx.doi.org/10.2166/wst.2005.0105.
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In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.
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Xiaopu Dong. "Optimization of landscape garden greening design based on multi objective genetic algorithm." Journal of Electrical Systems 20, no. 6s (April 29, 2024): 226–36. http://dx.doi.org/10.52783/jes.2632.
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This paper presents a novel approach to optimize landscape garden greening design using a multi-objective genetic algorithm (MOGA)[1]. Incorporating genetic algorithms into landscape architecture offers a promising avenue for efficiently navigating the complex and multidimensional design space inherent in green infrastructure projects. Through a comprehensive bibliometric analysis of existing literature, this study synthesizes key insights into the application of genetic algorithms in landscape design and identifies gaps for further exploration[2]. Leveraging the evolutionary process of genetic algorithms, our methodology focuses on simultaneously optimizing multiple objectives such as biodiversity conservation, aesthetic appeal, water efficiency, and ecosystem services provisioning[3]. By iteratively evolving and selecting landscape configurations based on fitness criteria derived from these objectives, the MOGA enables designers to explore a diverse range of design alternatives and identify Pareto-optimal solutions that balance competing priorities. The integration of genetic algorithms into landscape design facilitates an iterative and adaptive design process, allowing for the exploration of complex trade-offs and the generation of innovative design solutions. Through a case study application, we demonstrate the effectiveness of the MOGA approach in optimizing landscape garden greening designs, showcasing its potential to enhance sustainability, resilience, and functionality in urban green spaces. This research contributes to the growing body of knowledge on computational design methods in landscape architecture and provides valuable insights for practitioners and researchers seeking to leverage genetic algorithms for optimizing green infrastructure projects.
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Liu, Tung Kuan, Hsin Yuan Chang, Wen Ping Wu, Chiu Hung Chen, and Min Rong Ho. "Evaluated Preference Genetic Algorithm and its Engineering Applications." Key Engineering Materials 467-469 (February 2011): 2129–36. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2129.
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This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.
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Melaku, Shimeles Demissie, and Hae-Dong Kim. "Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm." Remote Sensing 15, no. 6 (March 13, 2023): 1572. http://dx.doi.org/10.3390/rs15061572.
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The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat’s ability to perform a multi-missions.
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Chui, Kwok Tai, Miltiadis D. Lytras, and Ryan Wen Liu. "A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM." Sensors 20, no. 5 (March 7, 2020): 1474. http://dx.doi.org/10.3390/s20051474.
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Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.
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Zhong, Ru, Jian Ping Wu, and Yi Man Du. "Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 253-255 (December 2012): 1356–59. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1356.
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When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.
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Liao, Lingxia, Victor C. M. Leung, Zhi Li, and Han-Chieh Chao. "Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks." Symmetry 13, no. 7 (June 24, 2021): 1133. http://dx.doi.org/10.3390/sym13071133.
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To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.
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Bonakdari, Hossein, Isa Ebtehaj, and Azam Akhbari. "Multi-objective evolutionary polynomial regression-based prediction of energy consumption probing." Water Science and Technology 75, no. 12 (March 16, 2017): 2791–99. http://dx.doi.org/10.2166/wst.2017.158.
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Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.
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Roy, Dilip, Sujit Biswas, Mohamed Mattar, Ahmed El-Shafei, Khandakar Murad, Kowshik Saha, Bithin Datta, and Ahmed Dewidar. "Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models." Water 13, no. 21 (November 6, 2021): 3130. http://dx.doi.org/10.3390/w13213130.
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Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs.
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Chen, Bo, Jiyue Yang, Haoyu Tang, Yahang Wu, and Haoran Zhang. "Optimization of Flexible Rotor for Ultrasonic Motor Based on Response Surface and Genetic Algorithm." Micromachines 16, no. 1 (December 31, 2024): 54. https://doi.org/10.3390/mi16010054.
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The flexible rotor, as a crucial component of the traveling wave rotary ultrasonic motor, effectively reduces radial friction. However, issues such as uneven contact between the stator and rotor, as well as rotor-deformation-induced stress, still persist. This paper presents an optimization method that combines the Kriging response surface model with a multi-objective genetic algorithm (MOGA). Drawing on the existing rotor structure, a novel rotor design is proposed to match the improved TRUM60 stator. During the optimization process, the contact surface between the stator and rotor is taken as the optimization target, and an objective function is established. The Kriging response surface model is constructed using Latin hypercube sampling, and an MOGA is employed to optimize this model, allowing the selection of the optimal balanced solution from multiple candidate designs. Following stator optimization, the objective function value decreased from 0.631 to 0.036, and the maximum contact stress on the rotor inner ring was reduced from 32.77 MPa to 9.96 MPa. Experimental validation confirmed the reliability of this design, significantly improving the overall performance and durability of the motor.
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Mahmood, Tahera. "Data Dissemination Scheme for VANET using Genetic algorithm and Particle Swarm Optimization." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 322–28. http://dx.doi.org/10.35940/ijrte.a5970.0510121.
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A vehicular ad hoc network (VANET) consist of moving vehicles connected via wireless technology e.g., Wireless Access in Vehicular Environment (WAVE) for the aim of exchanging information. Therefore data dissemination in VANET has become issue of debate for researcher. In VANET broadcasting play an important role. The aim of VANET is to ensure passenger safety through emergency message. With multiple objectives broadcast storm is assumed to be an NP-Hard problem. In this paper we propose DDV algorithm to solve broadcast storm problem. Fitness function has used to optimize the objective of proposed algorithm. The proposed algorithm producing better optimization results. We are considering a highway scenario in city with dynamic rotation, to evaluate the performance of the DDV algorithm we compare the result with Smart flooding techniques, MOGA (Multi Objective Genetic algorithm) [1] and EEADP. Our result show the better performance in terms of reduce the number of retransmission, increase the packet delivery ratio and provide better throughput.
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Bhardwaj, Shikha, Prof Neeraj Bhargava, and Dr Ritu Bhargava. "Genetic Algorithms: A Solution to Fiber Reinforced Composite Drilling Challenges." International Journal of Emerging Science and Engineering 11, no. 6 (May 30, 2023): 1–5. http://dx.doi.org/10.35940/ijese.f2548.0511623.
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Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.
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Vaghela, Naresh R., and Kaushik Nath. "Modelling and Multi-Objective Optimization of Continuous Indirect Electro-Oxidation Process for RTB21 Dye Wastewater Using ANN-GA Approach." Acta Chimica Slovenica 69, no. 2 (June 15, 2022): 304–15. http://dx.doi.org/10.17344/acsi.2021.7146.
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A continuous indirect electro-oxidation (EO) process was developed using graphite electrode to investigate the treatability of reactive turquoise blue RTB21 dye wastewater under specific operating conditions of initial pH, current density, hydraulic retention time (HRT), and electrolyte (NaCl) concentration. The experiments were performed in accordance with the central composite design (CCD), and the findings were used to create a model utilizing artificial neural networks (ANNs). According to the predicted findings of the ANN model, the MSE values for colour and COD removal efficiencies were estimated to be 0.748 and 0.870, respectively, while the R2 values were 0.9999 and 0.9998, respectively. The Multi-objective optimization using genetic algorithm (MOGA) over the ANN model maximizes the multiple responses: colour and COD removal efficiency (%). The MOGA generates a non-dominated Pareto front, which provides an insight into the process’s optimum operating conditions.
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Saraswat, Shelesh Krishna, Vinay Kumar Deolia, and Aasheesh Shukla. "Allocation of power in NOMA based 6G-enabled internet of things using multi-objective based genetic algorithm." Journal of Electrical Engineering 74, no. 2 (April 1, 2023): 95–101. http://dx.doi.org/10.2478/jee-2023-0012.
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Abstract Sixth generation (6G)-enabled internet of things (IoT) requires significant spectrum resources to deliver spectrum availability for massive IoT’s nodes. But the existing orthogonal multiple access limits the full utilization of limited spectrum resources. The non-orthogonal multiple access (NOMA) exploits the potential of power domain to improve the connectivity for 6G-enabled IoT. An efficient quality of service (QoS) aware power allocation approach is required to enhance the spectral efficiency and energy of NOMA based 6G-enabled IoT nodes. The multi-objective genetic algorithm (MOGA) is used to resolve the non-convex problem by considering the successive interference cancellation (SIC), QoS, and transmission power. Extensive experiments are drawn by using the Monte Carlo simulation to evaluate the significant improvement of the proposed model. Experimental results indicate that the proposed power allocation model provides good performance of the NOMA based IoT network.
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Majumder, Arindam. "Optimization of Modern Manufacturing Processes Using Three Multi-Objective Evolutionary Algorithms." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 96–124. http://dx.doi.org/10.4018/ijsir.2021070105.
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The optimization in manufacturing processes refers to the investigation of multiple responses simultaneously. Therefore, it becomes very necessary to introduce a technique that can solve the multiple response optimization problem efficiently. In this study, an attempt has been taken to find the application of three newly introduced multi-objective evolutionary algorithms, namely multi-objective dragonfly algorithm (MODA), multi-objective particle swarm optimization algorithm (MOPSO), and multi-objective teaching-learning-based optimization (MOTLBO), in the modern manufacturing processes. For this purpose, these algorithms are used to solve five instances of modern manufacturing process—CNC process, continuous drive friction welding process, EDM process, injection molding process, and friction stir welding process—during this study. The performance of these algorithms is measured using three parameters, namely coverage to two sets, spacing, and CPU time. The obtained experimental results initially reveal that MODA, MOPSO, and MOTLBO provide better solutions as compared to widely used nondominated sorting genetic algorithm II (NSGA-II). Moreover, this study also shows the superiority of MODA over MOPSO and MOTLBO while considering coverage to two sets and CPU time. Further, in terms of spacing a marginally inferior performance is observed in MODA as compared to MOPSO and MOTLBO.
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Elgammal, Adel. "OPTIMAL CONTROL OF A HYBRID PV-WIND POWER SYSTEM FOR ISLANDED HYBRID MICROGRID USING MOGA-FUZZY LOGIC CONTROLLER." International Journal of Engineering Applied Sciences and Technology 09, no. 03 (August 4, 2024): 08–15. http://dx.doi.org/10.33564/ijeast.2024.v09i03.002.
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The paper presents a comprehensive approach to managing the complexities associated with the integration of photovoltaic (PV) and wind energy systems in islanded microgrid environments. These microgrids, often isolated from the main power grid, face unique challenges due to the variability and intermittency of renewable energy sources, which can lead to instability and inefficiencies in power supply. To address these challenges, the study introduces a novel control strategy that combines a Multi-Objective Genetic Algorithm (MOGA) with a Fuzzy Logic Controller. The MOGA is utilized to explore a broad solution space and identify optimal control parameters that balance multiple objectives, including minimizing power fluctuations, maximizing the utilization of renewable energy, and ensuring a stable and reliable power supply. This optimization is crucial in islanded microgrids, where the lack of a larger grid connection necessitates highly efficient and responsive energy management systems to maintain stability. The Fuzzy Logic Controller, on the other hand, provides a flexible and adaptive control mechanism that responds to the dynamic and often unpredictable nature of renewable energy generation. By interpreting input variables in a way that mimics human decision-making, the Fuzzy Logic Controller can effectively handle the inherent uncertainties and non-linearities in the power system, adjusting the operation of the PV and wind power sources, as well as any supplementary energy storage systems, to optimize performance. This adaptive capability is particularly beneficial in scenarios where rapid changes in weather conditions can significantly impact energy generation and consumption patterns. The integration of MOGA with Fuzzy Logic not only enhances the decisionmaking process but also allows for the simultaneous consideration of multiple objectives, which is a critical advancement over traditional single-objective optimization techniques. This dual approach ensures that the hybrid PV-wind power system operates at its highest efficiency, balancing the need for sustainability with the practical requirements of reliability and economic viability. The results from extensive simulations, which model various operational scenarios and disturbances, demonstrate that the proposed MOGA-Fuzzy Logic Controller significantly improves the stability and efficiency of the islanded microgrid. The system is shown to effectively manage power flows, reduce dependency on fossil fuel-based backup generators, and increase the overall penetration of renewable energy. The proposed MOGA-Fuzzy Logic Controller stands out as a promising solution for the optimal control of hybrid PV-wind power systems, offering a viable pathway for achieving sustainable energy goals in islanded and other decentralized grid settings. This work not only advances academic knowledge but also has practical implications for the design and operation of future energy systems, making it a significant contribution to the field of renewable energy and power system engineering.
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Narayanan, Rama Chandran, Narayanan Ganesh, Robert Čep, Pradeep Jangir, Jasgurpreet Singh Chohan, and Kanak Kalita. "A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications." Mathematics 11, no. 10 (May 15, 2023): 2301. http://dx.doi.org/10.3390/math11102301.
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In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.
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Zhang, Zhimei, and Xiaobo Wang. "Fatigue Life Prediction of FRP-Strengthened Reinforced Concrete Beams Based on Soft Computing Techniques." Materials 18, no. 2 (January 7, 2025): 230. https://doi.org/10.3390/ma18020230.
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This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams.
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Xuebin, Li. "Multiobjective Optimization and Multiattribute Decision Making Study of Ship’s Principal Parameters in Conceptual Design." Journal of Ship Research 53, no. 02 (June 1, 2009): 83–92. http://dx.doi.org/10.5957/jsr.2009.53.2.83.
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Numerous real-world problems relating to ship design are characterized by many alternatives as well as multiple conflicting objectives. Ship design is a complex endeavor requiring the successful coordination of many different disciplines, both technical and nontechnical. Conceptual design is the least defined stage of the ship design process and seeks to define the basic payloads and ship size characteristics. A hybrid approach for multiobjective optimization study of ship's principal parameters in conceptual design is proposed in the present analysis. In the first stage, a multiple objective genetic algorithm (MOGA) is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multiattribute decision making (MADM) approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. A bulk carrier example, with 6 parameters, 3 criteria, and 14 constraints is conducted to illustrate the analysis process in present study. Pareto frontiers are obtained, and the ranking of the Pareto solution set is based on entropy weight and TOPSIS method. The ideal solution is compared with those from classic multiobjective methods.
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Gasparetto, Victor E. L., Jackson Reid, William P. Parsons, Mostafa S. A. ElSayed, Mohamed Saad, Stephen Shieldand, Gary L. Brown, and Lawrence M. Hilliard. "Multi-Objective Design Optimization of Multiple Tuned Mass Dampers for Attenuation of Dynamic Aeroelastic Response of Aerospace Structures." Aerospace 10, no. 3 (February 27, 2023): 235. http://dx.doi.org/10.3390/aerospace10030235.
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This paper proposes a design procedure to determine the optimal configuration of multi-degrees of freedom (MDOF) multiple tuned mass dampers (MTMD) to mitigate the global dynamic aeroelastic response of aerospace structures. The computation of the aerodynamic excitations is performed considering two models of atmospheric disturbances, namely, the Power Spectral Density (PSD) modelled with the Davenport Spectrum (DS) and the Tuned Discrete Gust (TDG) with the one-minus cosine profile. In order to determine the optimum sets of MTMD, a Multi-objective design Optimization considering Genetic Algorithm (MOGA) is implemented, where the selected fitness functions for the analysis are the minimization of the total mass of the resonators as well as the concurrent minimization of the peak displacements of a specified structural node in all translational degrees of freedom. A case study is presented to demonstrate the proposed methodology, where the optimal sets of MTMD are determined for the concurrent minimization of the pointing error of a truss-like antenna structure as well as the mass of the considered MTMD. It is found that the placement of the MTMD in the primary reflector of the antenna structure provided a maximum reduction in the pointing error of 62.0% and 39.2%, considering the PSD and the TDG models, respectively. Finally, this paper presents an advanced framework to estimate optimal parameters of MTMD control devices under convoluted loading cases as an initial step towards the use of such passive systems in applications that commonly employ active or semi-active solutions.
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Dang, Minh Phung, Hieu Giang Le, Ngoc Phat Nguyen, Ngoc Le Chau, and Thanh-Phong Dao. "Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms." Computational Intelligence and Neuroscience 2022 (December 1, 2022): 1–18. http://dx.doi.org/10.1155/2022/9151146.
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This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN) and particle swarm optimization (PSO) methods. Next, the PSO is hybridized with the grey wolf optimization, namely PSO-GWO, which is applied to three scenarios in handling the single objective function. In order to search the multiple functions for the mechanism, the multiobjective optimization genetic algorithm (MOGA) is applied to the last scenario. The achieved results showed that the fitness functions are well-formulated using the PSO-based ANN method. In the scenario 1, the stroke achieved by the PSO-GWO (1852.9842 μm) is better than that gained from the GWO (1802.8087 μm). In the scenarios 2, the stress gained from the PSO-GWO (243.3183 MPa) is lower than that achieved from the GWO (245.0401 MPa). In the scenario 3, the safety factor retrieved from the PSO-GWO (1.9767) is greater than that achieved from the GWO (1.9278). In the scenario 4, by using MOGA, the optimal results found that the stroke is about (1741.3 μm) and the safety factor is 1.8929. The prediction results are well-fitted with the numerical and experimental verifications. The results of this paper are expected to facilitate the synthesis and analysis of compliant mechanisms and related engineering designs.
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Nekoui, Mohammad Ali, and Hassan Heidari Jame Bozorgi. "Weighting Matrix Selection Method for LQR Design Based on a Multi-Objective Evolutionary Algorithm." Advanced Materials Research 383-390 (November 2011): 1047–54. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1047.
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This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic Algorithm LQR ( NSGA- LQR )weighting matrices design approach.
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Zhang, Zhenqiang, Sile Ma, and Xiangyuan Jiang. "Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms." Mathematics 10, no. 24 (December 12, 2022): 4714. http://dx.doi.org/10.3390/math10244714.
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Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.
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Ullah, Kalim, Taimoor Ahmad Khan, Ghulam Hafeez, Imran Khan, Sadia Murawwat, Basem Alamri, Faheem Ali, Sajjad Ali, and Sheraz Khan. "Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid." Energies 15, no. 19 (September 21, 2022): 6900. http://dx.doi.org/10.3390/en15196900.
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Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.
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Ngo, Son Tung, Jafreezal Jaafar, Aziz Abdul Izzatdin, Giang Truong Tong, and Anh Ngoc Bui. "Some metaheuristic algorithms for solving multiple cross-functional team selection problems." PeerJ Computer Science 8 (August 9, 2022): e1063. http://dx.doi.org/10.7717/peerj-cs.1063.
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We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).
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Castellanos-Alvarez, Alejandro, Laura Cruz-Reyes, Eduardo Fernandez, Nelson Rangel-Valdez, Claudia Gómez-Santillán, Hector Fraire, and José Alfredo Brambila-Hernández. "A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification." Mathematical and Computational Applications 26, no. 2 (March 30, 2021): 27. http://dx.doi.org/10.3390/mca26020027.
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Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.
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Kohani, Saeid, Peng Zong, and Fengfan Yang. "Design Coverage Optimization Based on Position of Constellations and Cost of the Launch Vehicle." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (November 17, 2021): 1160–90. http://dx.doi.org/10.37394/232015.2021.17.107.
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This research will analyze the tradeoffs between coverage optimization based on Position dilution of precision (PDOP) and cost of the launch vehicle. It adopts MATLAB and STK tools along with multiple objective genetic algorithms (MOGA) to explore the trade space for the constellation designs at different orbital altitudes. The objective of optimal design solutions is inferred to determine the economic and efficient LEO, MEO, HEO or hybrid constellations and simulation results are presented to optimize the design of satellite constellations. The benefits of this research are the optimization of satellite constellation design, which reduces costs and increases regional and global coverage with the least number of satellites. The result of this project is the optimization of the number of constellation satellites in several orbital planes in LEO orbit. Validations are based on reviewing the results of several simulations. The results of graphs and tables are presented in the last two sections and are taken from the results of several simulations.
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Tran, Duong, Sajib Chakraborty, Yuanfeng Lan, Joeri Van Mierlo, and Omar Hegazy. "Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization." Applied Sciences 8, no. 8 (August 11, 2018): 1351. http://dx.doi.org/10.3390/app8081351.
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DC/DC Multiport Converters (MPC) are gaining interest in the hybrid electric drivetrains (i.e., vehicles or machines), where multiple sources are combined to enhance their capabilities and performances in terms of efficiency, integrated design and reliability. This hybridization will lead to more complexity and high development/design time. Therefore, a proper design approach is needed to optimize the design of the MPC as well as its performance and to reduce development time. In this research article, a new design methodology based on a Multi-Objective Genetic Algorithm (MOGA) for non-isolated interleaved MPCs is developed to minimize the weight, losses and input current ripples that have a significant impact on the lifetime of the energy sources. The inductor parameters obtained from the optimization framework is verified by the Finite Element Method (FEM) COMSOL software, which shows that inductor weight of optimized design is lower than that of the conventional design. The comparison of input current ripples and losses distribution between optimized and conventional designs are also analyzed in detailed, which validates the perspective of the proposed optimization method, taking into account emerging technologies such as wide bandgap semiconductors (SiC, GaN).
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Wang, Dawei, Lei Zhang, Fengfu Yang, Jinrong Yang, Yang Wu, and Peng Cao. "Dynamic Response and Optimal Design of Radio Telescope Structure under Wind Load Excitation." Buildings 13, no. 11 (November 1, 2023): 2764. http://dx.doi.org/10.3390/buildings13112764.
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The dynamic response of a radio telescope structure under wind load excitation significantly impacts the accuracy of signal reception. To address this issue, this study established a parametric finite element model of a radio telescope to simulate its dynamic response under wind load excitation. An improved Latin hypercube sampling method was applied in the design of experiments (DOEs) to optimize the structural dimensional parameters of various components of the radio telescope with the aim of reducing the dynamic response to wind load. A response surface model and multi-objective genetic algorithm (MOGA) were employed for multi-objective structural optimization of the radio telescope structure. The findings reveal that the thickness of the stiffening ribs, the length of the side of the square hollow pole, the thickness of the middle pole, and the inner diameter of the thin pole are the most influential structural parameters affecting the first-order frequency (F1), second-order frequency (F2), maximum deformation in the x-direction (DX), and maximum deformation in the z-direction (DZ) of the radio telescope, respectively. Optimizing the radio telescope results in a 40.00% improvement in F1 and a 24.16% enhancement in F2, while reducing DX by 43.94% and DZ by 64.25%. The study outcomes offer a comprehensive scheme for optimizing the structural dimensional parameters of various radio telescope components in regions characterized by multiple wind fields.
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Garces-Jimenez, Alberto, Jose-Manuel Gomez-Pulido, Nuria Gallego-Salvador, and Alvaro-Jose Garcia-Tejedor. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study." Mathematics 9, no. 18 (September 7, 2021): 2181. http://dx.doi.org/10.3390/math9182181.
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Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.
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Greve, Gabriel H., Kenneth M. Hopkinson, and Gary B. Lamont. "Evolutionary sensor allocation for the Space Surveillance Network." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 3 (July 21, 2017): 303–22. http://dx.doi.org/10.1177/1548512917712614.
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The congested exosphere continues to contain more satellites and debris, raising the potential for destructive collisions. The Special Perturbations (SP) Tasker algorithm currently assigns the ground sensors tasks to track object locations. Accurate locations help avoid collisions. However, the SP Tasker ignores priority, which is the satellite’s importance factor. This article introduces the Evolutionary Algorithm Tasker (EAT) to solve the Satellite Sensor Allocation Problem (SSAP), which is a hybrid Evolutionary Strategy and Genetic Algorithm concept including specific techniques to explore the solution space and exploit the best solutions found. This approach goes beyond the current method, which does not include priority and other methods from the literature that have been applied to small-scale simulations. The SSAP model implementation extends Multi-Objective Evolutionary Algorithms (MOEAs) from the literature while accounting for priorities. Multiple real-world factors are considered, including each sensor’s field-of-view, the orbital opportunities to track a satellite, the capacity of the sensor, and the relative priority of the satellites. The single objective EAT is statistically compared to the SP Tasker algorithm. Simulations show that both the EAT and MOEA approaches effectively use priority in the core tasking algorithms to ensure that higher priority satellites are tracked.
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Duan, Lijia, Zekun Guo, Gareth Taylor, and Chun Sing Lai. "Multi-Objective Optimization for Solar-Hydrogen-Battery-Integrated Electric Vehicle Charging Stations with Energy Exchange." Electronics 12, no. 19 (October 5, 2023): 4149. http://dx.doi.org/10.3390/electronics12194149.
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The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.
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Qin, Huaiyu, Buhui Zhao, Leijun Xu, and Xue Bai. "Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks." Remote Sensing 13, no. 13 (July 3, 2021): 2611. http://dx.doi.org/10.3390/rs13132611.
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Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.
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Shieh, Meng-Dar, Yongfeng Li, and Chih-Chieh Yang. "Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/5187521.
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Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs). Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO). However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM). First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II) as a multiobjective evolutionary algorithm (MOEA) to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP) to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.
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Wang, Feiyue, Ziling Xie, Zhongwei Pei, and Dingli Liu. "Emergency Relief Chain for Natural Disaster Response Based on Government-Enterprise Coordination." International Journal of Environmental Research and Public Health 19, no. 18 (September 7, 2022): 11255. http://dx.doi.org/10.3390/ijerph191811255.
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Public health and effective risk response cannot be promoted without a coordinated emergency process during a natural disaster. One primary problem with the emergency relief chain is the homogeneous layout of rescue organizations and reserves. There is a need for government-enterprise coordination to enhance the systemic resilience and demand orientation. Therefore, a bi-level multi-phase emergency plan model involving procurement, prepositioning and allocation is proposed. The tradeoff of efficiency, economy and fairness is offered through the multi-objective cellular genetic algorithm (MOCGA). The flood emergency in Hunan Province, China is used as a case study. The impact of multi-objective and coordination mechanisms on the relief chain is discussed. The results show that there is a significant boundary condition for the coordinated location strategy of emergency facilities and that further government coordination over the transition phase can generate optimal relief benefits. Demand orientation is addressed by the proposed model and MOCGA, with the realization of the process coordination in multiple reserves, optimal layout, and transition allocation. The emergency relief chain based on government-enterprise coordination that adapts to the evolution of disasters can provide positive actions for integrated precaution and health security.
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Yusuf, Aminu, Nevra Bayhan, Hasan Tiryaki, and Sedat Balllikaya. "MACHINE LEARNING AS A POWERFUL TOOL FOR PERFORMANCE PREDICTION AND OPTIMIZATION OF CONCENTRATED PHOTOVOLTAIC-THERMOELECTRIC SYSTEM." Konya Journal of Engineering Sciences 12, no. 2 (April 4, 2024): 478–93. http://dx.doi.org/10.36306/konjes.1396648.
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Because there is a critical necessity to ensure the optimal operation of concentrated photovoltaic-thermoelectric (CPV-TE) systems, various optimization methods such as Paretosearch (PS), Multi-objective genetic algorithm (MOGA), and the hybrid Goal Attainment – Multi-objective genetic algorithm (GOAL-MOGA) are commonly employed. These approaches aim to enhance both the output power and energy efficiency of CPV-TE systems. By combining the Pareto fronts generated by MOGA and GOAL-MOGA, 19 distinct machine learning (ML) algorithms were trained. The findings demonstrate that the Artificial Neural Network (ANN) ML algorithm outperforms others, displaying an average prediction error of 0.0692% on the test dataset. In addition to its prediction capability, the ANN-based ML model can be viewed as an optimization model since it produces optimized outputs similar to those from MOGA and GOAL-MOGA. The ANN-based ML algorithm performs better when trained on a combined dataset from both MOGA and GOAL-MOGA compared to using either MOGA or GOAL-MOGA alone. To enhance the optimization capability of the ANN-based ML algorithm further, more Pareto fronts from other optimization techniques can be added.
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Zou, Jin, and Yong Gang Wu. "An Advanced Multi-Objective Genetic Algorithm Based on Borda Number." Applied Mechanics and Materials 204-208 (October 2012): 4909–15. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4909.
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When applied to Multi-Objective Decision Making (MODM), genetic algorithm is plagued with two problems: how to appreciate non-inferior solutions and how to store them. Using Borda number as the fitness of a chromosome, an advanced Multi-Objective Genetic Algorithm ( MOGA) is provided in this paper, which can solve these problems in an easier way; and with the characteristic of Genetic Algorithm (GA) of producing a number of feasible solutions, this approach is able to obtain the set of non-inferior solutions without information of the decision-maker’s preferences. Finally, a simulated example to apply this algorithm to a multipurpose reservoir’s operation is provided, indicating the feasibility and effectiveness of this advanced MOGA.
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Patel, Rahila, M. M. Raghuwanshi, and Latesh Malik. "Multi-Objective Genetic Algorithm with Strategies for Dying of Solution." International Journal of Applied Evolutionary Computation 5, no. 1 (January 2014): 69–85. http://dx.doi.org/10.4018/ijaec.2014010105.
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Genetic Algorithm (GA) mimics natural evolutionary process. Since dying of an organism is important part of natural evolutionary process, GA should have some mechanism for dying of solutions just like GA have crossover operator for birth of solutions. In nature, occurrence of event of dying of an organism has some reasons like aging, disease, malnutrition and so on. In this work we propose three strategies of dying or removal of solution from next generation population. Multi-objective Genetic Algorithm (MOGA) takes decision of removal of solution, based on one of these three strategies. Experiments were performed to show impact of dying of solutions and dying strategies on the performance of MOGA.
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Khan, Ihtisham Ullah, Dr Gul Rukh, and Mian Farhan Ullah. "A multi-objective strategy for cost-effective microgrid solutions based on renewable energy sources." International Conference on Applied Engineering and Natural Sciences 1, no. 1 (July 22, 2023): 1057–61. http://dx.doi.org/10.59287/icaens.1128.
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With the rapid urbanization and increasing energy demands, microgrids are recognizing renewable energy sources (RESs) as a valuable power generation option. However, efficiently managing the energy cost poses a significant challenge in integrating RESs with microgrids. To address this challenge, this study presents a novel approach utilizing a cost-effective multi-objective genetic algorithm (MOGA) to optimize power allocation among diverse generation units within the microgrid. The proposed MOGA algorithm aims to minimize generation costs by efficiently distributing the generated power from different sources in the microgrid vs the CO2 emission. By leveraging the genetic algorithm population, MOGA generates a diverse set of non-dominated solutions. Simulation results demonstrate the effectiveness of the proposed approach in reducing the cost of RESs in microgrids, surpassing the performance of other multi-objective optimization methods such as multi-objective particle swarm (MOPSO) and multi-objective wind-driven optimization (MOWDO).
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Гарагулова, Анастасия Керимовна, Дарья Олеговна Горбачева, and Денис Владимирович Чирков. "Comparative analysis of MOGA and NSGA-II on the case study of optimization for the profile of the hydraulic turbine runner." Вычислительные технологии, no. 5(23) (November 2, 2018): 21–36. http://dx.doi.org/10.25743/ict.2018.23.5.003.
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Проведено сравнение генетических алгоритмов MOGA и NSGA-II на тестовых задачах и задаче оптимизации формы рабочего колеса гидротурбины. Рассмотрен модифицированный алгоритм NSGA-IIm, в котором операторы рекомбинации и мутации заимствованы из MOGA. Для сравнения скорости сходимости алгоритмов использована метрика, характеризующая расстояние от приближенного фронта Парето до точного. Представлены результаты решения тестовой задачи Purpose. The main goal of this article is to compare two popular multi-objective genetic algorithms MOGA and NSGA-II by solving test problems and a practical problem of hydraulic turbine runner optimization. Modification of NSGA-II called NSGA-IIm is also considered. The major problem is to compare convergence rates of approximate solution to the exact Pareto front. Methodology. The genetic algorithms MOGA and NSGA-II are described in detail. The modified algorithm NSGA-IIm is NSGA-II with the recombination and mutation operators taken from the MOGA algorithm. The known problems ZDT3 (2 objectives, 30 and 12 parameters, no constraints) and OSY (2 objectives, 6 parameters, 6 constraints) are taken as test problems. These algorithms are compared by solving runner optimization problem with 24 free parameters and 2 or 3 objectives. To compare the algorithms, a metric characterizing the distance from the approximate Pareto front to the exact one is introduced. Since the algorithms are stochastic in nature, the value of the metric was averaged over 100 runs of the algorithm. In the two-objective runner optimization problem the metric value was averaged over 3 runs of the algorithm. Findings. Solving the test problems, it was found that in the first 50 generations the MOGA algorithm converges faster than other algorithms, but after 50 generations the NSGA-II algorithm has shown the best result. The MOGA algorithm gives an approximate front containing more solutions than NSGA-II. When solving the two-objective and the three-objective runner optimization problem the similar results were obtained. The approximate Pareto front, obtained by the MOGA algorithm, is distributed more uniformly than other algorithms and contains a larger number of solutions. The advantage of the algorithms NSGA-II and NSGA-IIm is a slightly better definition of extreme values of target functionals. However, the obtained differences are not very significant. Originality/value. Obtained results show that both MOGA and NSGA-II are very similar in terms of convergence rate and can be applied for solving complex engineering problems.
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Nusen, Pornpote, Wanarut Boonyung, Sunita Nusen, Kriengsak Panuwatwanich, Paskorn Champrasert, and Manop Kaewmoracharoen. "Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm." Applied Sciences 11, no. 11 (May 21, 2021): 4716. http://dx.doi.org/10.3390/app11114716.
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Renovation is known to be a complicated type of construction project and prone to errors compared to new constructions. The need to carry out renovation work while keeping normal business activities running, coupled with strict governmental building renovation regulations, presents an important challenge affecting construction performance. Given the current availability of robust hardware and software, building information modeling (BIM) and optimization tools have become essential tools in improving construction planning, scheduling, and resource management. This study explored opportunities to develop a multi-objective genetic algorithm (MOGA) on existing BIM. The data were retrieved from a renovation project over the 2018–2020 period. Direct and indirect project costs, actual schedule, and resource usage were tracked and retrieved to create a BIM-based MOGA model. After 500 generations, optimal results were provided as a Pareto front with 70 combinations among total cost, time usage, and resource allocation. The BIM-MOGA can be used as an efficient tool for construction planning and scheduling using a combination of existing BIM along with MOGA into professional practices. This approach would help improve decision-making during the construction process based on the Pareto front data provided.
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Zerdali, Emrah, and Aycan Gurel. "Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms." Power Electronics and Drives 8, no. 1 (January 1, 2023): 325–34. http://dx.doi.org/10.2478/pead-2023-0020.
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Abstract In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.
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Berardi, L., O. Giustolisi, D. A. Savic, and Z. Kapelan. "An effective multi-objective approach to prioritisation of sewer pipe inspection." Water Science and Technology 60, no. 4 (April 1, 2009): 841–50. http://dx.doi.org/10.2166/wst.2009.432.
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The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimization problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.
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Yoshida, Toru, and Tomohiro Yoshikawa. "Analysis of Pareto Solutions Based on Non-Correspondence in Spread Between Objective Space and Design Variable Space." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (September 20, 2015): 681–87. http://dx.doi.org/10.20965/jaciii.2015.p0681.
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Recently, many studies have been conducted on Multi-Objective Genetic Algorithm (MOGA), in which Genetic Algorithms are applied to Multi-objective Optimization Problems (MOPs). Among various applications, MOGA is also applied to engineering design problems, which require not only high-performance Pareto solutions to be obtained, but also an analysis of the obtained Pareto solutions and extraction of design knowledge about the problem itself. In order to analyze the Pareto solutions obtained by MOGA, it is necessary to consider the objective space and the design variable space. The aim of this study is to extract and analyze solutions of relevant interest to designers. In this paper, we propose three solutions to analyze and extract design knowledge from MOGA. (1) We define “Non-Correspondence in Spread” between the objective space and the design variable space. (2) We try to extract the Non-Correspondence area in Spread using the index defined in this paper. (3) We apply the defined index to genetic search to obtain Pareto solutions that have different design variables and similar fitness values. This paper applies the above index to the trajectory design optimization problem and extracts Non-Correspondence area in Spread from the obtained Pareto solutions. This paper also shows that robust Pareto solutions can be obtained using genetic search using the defined index.
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Gavrilenkov, Sergey I. "A Framework for Optimal Placement of Strain Gauges on Elastic Elements of Force Sensors Using Genetic Algorithms." ITM Web of Conferences 35 (2020): 04010. http://dx.doi.org/10.1051/itmconf/20203504010.
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This paper presents a digital education tool for learning the specifics and behavior of a multi-objective genetic algorithm (MOGA) used to solve the problem of optimal placement of strain gauges on the elastic element of a force sensor. The paper formulates the problem statement and specifies how this problem can be solved using the MOGA. For the problem, the design variables are the locations of strain gauges and angles at which they are positioned. The goal functions are the output signal of the sensor and the measurement error from bending moments, which can be caused by the off-centric application of load. The solution algorithm is implemented within a framework that can be used to investigate and learn how parameters of MOGA influence its performance. The framework is used to run computational experiments for the given problem to find the optimal placement of strain gauges on the elastic element of a given force sensor. The performance of the MOGA in solving this problem is compared to that of the traditional approach.
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Umair, Muhammad, Mirza Jahanzaib, and Saif Ullah. "MULTI-OBJECTIVE ENERGY EFFICIENT MIXED MODEL ASSEMBLY LINE SEQUENCING FOR SUSTAINABLE MANUFACTURING." NED University Journal of Research XVII, no. 2 (March 1, 2020): 47–60. http://dx.doi.org/10.35453/nedjr-ascn-2019-0005.
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The sustainability of manufacturing systems is not thoroughly investigated in the existing literature despite that fact that it affects service-oriented organisations. This paper addresses this gap by incorporating the criterion of energy consumption in mixed model assembly line sequencing (MMALS) to explore the potential for energy saving. Energy consumption was integrated with makespan and sequence dependent setup time of mixed models to achieve the sustainability in sequencing. A mathematical model was developed for the optimisation of energy efficient MMAL (EEMMAL). The multi-objective intelligent genetic algorithm (MOIGA) was proposed for minimisation of three conflicting objectives. A case study was conducted using centrifugal pump assembly line to test the performance of proposed MOIGA and the results were compared with those obtained from multi-objective genetic algorithm (MOGA). Finally, a trade-off analysis was conducted between total setup time, makespan (a service level measure on shop floor) and energy consumption (a factor of environmental sustainability). This analysis indicated the effectiveness of MOIGA (compared to MOGA) for EEMMALS problems.
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Saleem, Noman, Kashif Zafar, and Alizaa Sabzwari. "Enhanced Feature Subset Selection Using Niche Based Bat Algorithm." Computation 7, no. 3 (September 6, 2019): 49. http://dx.doi.org/10.3390/computation7030049.
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Redundant and irrelevant features disturb the accuracy of the classifier. In order to avoid redundancy and irrelevancy problems, feature selection techniques are used. Finding the most relevant feature subset that can enhance the accuracy rate of the classifier is one of the most challenging parts. This paper presents a new solution to finding relevant feature subsets by the niche based bat algorithm (NBBA). It is compared with existing state of the art approaches, including evolutionary based approaches. The multi-objective bat algorithm (MOBA) selected 8, 16, and 248 features with 93.33%, 93.54%, and 78.33% accuracy on ionosphere, sonar, and Madelon datasets, respectively. The multi-objective genetic algorithm (MOGA) selected 10, 17, and 256 features with 91.28%, 88.70%, and 75.16% accuracy on same datasets, respectively. Finally, the multi-objective particle swarm optimization (MOPSO) selected 9, 21, and 312 with 89.52%, 91.93%, and 76% accuracy on the above datasets, respectively. In comparison, NBBA selected 6, 19, and 178 features with 93.33%, 95.16%, and 80.16% accuracy on the above datasets, respectively. The niche multi-objective genetic algorithm selected 8, 15, and 196 features with 93.33%, 91.93%, and 79.16 % accuracy on the above datasets, respectively. Finally, the niche multi-objective particle swarm optimization selected 9, 19, and 213 features with 91.42%, 91.93%, and 76.5% accuracy on the above datasets, respectively. Hence, results show that MOBA outperformed MOGA and MOPSO, and NBBA outperformed the niche multi-objective genetic algorithm and the niche multi-objective particle swarm optimization.
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Parmar, Jignesh G., and Komal G. Dave. "Multi Objective Optimization of Machining Parameters in End Milling of AISI1020." Regular issue 10, no. 8 (June 30, 2021): 54–63. http://dx.doi.org/10.35940/ijitee.h9225.0610821.
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In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) have been used for the prediction and multi objective optimization of the end milling operation. Cutting speed, feed rate, depth of cut, material density and hardness have been considered as input variables. The predicted values and optimized results obtained through ANN and MOGA are compared with experimental results. A good correlation has been established between the ANN predicted values and experimental results with an average accuracy of 91.983% for material removal rate, 99.894% for tool life, 92.683% for machining time, 92.671% for tangential cutting force, 92.109% for power and 90.311% for torque. The MOGA approach has been proposed to obtain the cutting condition for optimization of each responses. The MOGA gives average accuracy of 96.801% for MRR, 99.653% for tool life, 86.833% for machining time, 93.74% for cutting force, 93.74% for power and 99.473% for torque. It concludes that ANN and MOGA are efficiently and effectively used for prediction and multi objective optimization of end milling operation for any selected materials before the experimental. Implementation of these techniques in industries before the experimentation is useful to reduce the lead time, experimental cost and power consumption also increase the productivity of the product.
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Fattah, Salmah, Ismail Ahmedy, Mohd Yamani Idna Idris, and Abdullah Gani. "Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 9 (September 2022): 155013292211235. http://dx.doi.org/10.1177/15501329221123533.
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Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.