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Ariza Vesga, Luis Felipe, Johan Sebastián Eslava Garzón, and Rafael Puerta Ramirez. "EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points." Ingeniería e Investigación 40, no. 3 (October 21, 2020): 55–69. http://dx.doi.org/10.15446/inginvestig.v40n3.82906.
Повний текст джерелаАнотація:
Multi-Objective and Many-objective Optimization problems have been extensively solved through evolutionary algorithms over a few decades. Despite the fact that NSGA-II and NSGA-III are frequently employed as a reference for a comparative evaluation of new evolutionary algorithms, the latter is proprietary. In this paper, we used the basic framework of the NSGA-II, which is very similar to the NSGA-III, with significant changes in its selection operator. We took the first front generated at the non-dominating sort procedure to obtain nonnegative and nonrepeated extreme points. This opensource version of the NSGA-III is called EF1-NSGA-III, and its implementation does not start from scratch; that would be reinventing the wheel. Instead, we took the NSGA-II code from the authors in the repository of the Kanpur Genetic Algorithms Laboratory to extend the EF1-NSGA-III. We then adjusted its selection operator from diversity, based on the crowding distance, to the one found on reference points and preserved its parameters. After that, we continued with the adaptive EF1-NSGA-III (A-EF1-NSGA-III), and the efficient adaptive EF1-NSGA-III (A2-EF1-NSGA-III), while also contributing to explain how to generate different types of reference points. The proposed algorithms resolve optimization problems with constraints of up to 10 objective functions. We tested them on a wide range of benchmark problems, and they showed notable improvements in terms of convergence and diversity by using the Inverted Generational Distance (IGD) and the HyperVolume (HV) performance metrics. The EF1-NSGA-III aims to resolve the power consumption for Centralized Radio Access Networks and the BiObjective Minimum DiameterCost Spanning Tree problems.
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Awad, Mahmoud, Mohamed Abouhawwash, and H. N. Agiza. "On NSGA-II and NSGA-III in Portfolio Management." Intelligent Automation & Soft Computing 32, no. 3 (2022): 1893–904. http://dx.doi.org/10.32604/iasc.2022.023510.
Повний текст джерела
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Sun, Xingping, Ye Wang, Hongwei Kang, Yong Shen, Qingyi Chen, and Da Wang. "Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem." Processes 9, no. 1 (December 29, 2020): 62. http://dx.doi.org/10.3390/pr9010062.
Повний текст джерелаАнотація:
Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists in various manufacturing processes. Researchers have always emphasized manufacturing efficiency and economic benefits while ignoring environmental impacts. In this paper, considering carbon emissions, a multi-objective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load is established. To solve this problem, we study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III). We find that some variants have better search capability in the MO-FJSP decision space. When the solution set is close to the Pareto frontier, the development ability of the NSGA-III variant in the decision space shows a difference. According to the research, we combine Pareto dominance with indicator-based thought. By utilizing three existing crossover operators, a modified NSGA-III (co-evolutionary NSGA-III (NSGA-III-COE) incorporated with the multi-group co-evolution and the natural selection is proposed. By comparing with three NSGA-III variants and five multi-objective evolutionary algorithms (MOEAs) on 27 well-known FJSP benchmark instances, it is found that the NSGA-III-COE greatly improves the speed of convergence and the ability to jump out of local optimum while maintaining the diversity of the population. From the experimental results, it can be concluded that the NSGA-III-COE has significant advantages in solving the low carbon MO-FJSP.
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Zou, Ying, Zuguo Chen, Shangyang Zhu, and Yingcong Li. "NSGA-III-Based Production Scheduling Optimization Algorithm for Pressure Sensor Calibration Workshop." Electronics 13, no. 14 (July 19, 2024): 2844. http://dx.doi.org/10.3390/electronics13142844.
Повний текст джерелаАнотація:
Although the NSGA-III algorithm is able to find the global optimal solution and has a good effect on the workshop scheduling optimization, the limitations in population diversity, convergence ability and local optimal solutions make it not applicable to certain situations. Thus, an improved NSGA-III workshop scheduling optimization algorithm is proposed in this work. It aims to address these limitations of the NSGA-III algorithm in processing workshop scheduling optimization. To solve the problem of individual elimination in the traditional NSGA-III algorithm, chaotic mapping is introduced in the improved NSGA-III algorithm to generate new offspring individuals and add the selected winning individuals to the offspring population as the parent population for the next iteration. The proposed algorithm was applied to a pressure sensor calibration workshop. A comparison with the traditional NSGA-III algorithm was conducted through a simulation analysis. The results show that the proposed algorithm can obtain a better convergence performance, improve the optimization ability and avoid falling into local optimal solutions.
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Geng, Huantong, Zhengli Zhou, Junye Shen, and Feifei Song. "A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization." Entropy 25, no. 1 (December 21, 2022): 13. http://dx.doi.org/10.3390/e25010013.
Повний текст джерелаАнотація:
The main challenge for constrained many-objective optimization problems (CMaOPs) is how to achieve a balance between feasible and infeasible solutions. Most of the existing constrained many-objective evolutionary algorithms (CMaOEAs) are feasibility-driven, neglecting the maintenance of population convergence and diversity when dealing with conflicting objectives and constraints. This might lead to the population being stuck at some locally optimal or locally feasible regions. To alleviate the above challenges, we proposed a dual-population-based NSGA-III, named DP-NSGA-III, where the two populations exchange information through the offspring. The main population based on the NSGA-III solves CMaOPs and the auxiliary populations with different environment selection ignore the constraints. In addition, we designed an ε-constraint handling method in combination with NSGA-III, aiming to exploit the excellent infeasible solutions in the main population. The proposed DP-NSGA-III is compared with four state-of-the-art CMaOEAs on a series of benchmark problems. The experimental results show that the proposed evolutionary algorithm is highly competitive in solving CMaOPs.
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Muteba Mwamba, John Weirstrass, Leon Mishindo Mbucici, and Jules Clement Mba. "Multi-Objective Portfolio Optimization: An Application of the Non-Dominated Sorting Genetic Algorithm III." International Journal of Financial Studies 13, no. 1 (January 28, 2025): 15. https://doi.org/10.3390/ijfs13010015.
Повний текст джерелаАнотація:
This study evaluates the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) in comparison to the traditional Mean–Variance optimization method for financial portfolio management. Leveraging a dataset of global financial assets, we applied both approaches to optimize portfolios across multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III significantly outperforms the Mean–Variance method by generating a more diverse set of Pareto-optimal portfolios. Portfolios optimized with NSGA-III exhibited superior performance, achieving higher Sharpe ratios, more favorable skewness, and reduced kurtosis, indicating a better balance between risk and return. Moreover, NSGA-III’s capability to handle conflicting objectives underscores its utility in navigating complex financial environments and enhancing portfolio resilience. In contrast, while the Mean–Variance method effectively balances risk and return, it demonstrates limitations in addressing higher-order moments of the return distribution. These results emphasize the potential of NSGA-III as a robust and comprehensive tool for portfolio optimization in modern financial markets characterized by multifaceted objectives.
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Qu, Zhanghao, Peng Zhang, Yaohua Hu, Huanbo Yang, Taifeng Guo, Kaili Zhang, and Junchang Zhang. "Optimal Design of Agricultural Mobile Robot Suspension System Based on NSGA-III and TOPSIS." Agriculture 13, no. 1 (January 14, 2023): 207. http://dx.doi.org/10.3390/agriculture13010207.
Повний текст джерелаАнотація:
The stability of vehicles is influenced by the suspension system. At present, there are many studies on the suspension of traditional passenger vehicles, but few are related to agricultural mobile robots. There are structural differences between the suspension system of agricultural mobile robots and passenger vehicles, which requires structural simplification and modelling concerning suspension of agricultural mobile robots. This study investigates the optimal design for an agricultural mobile robot’s suspension system designed based on a double wishbone suspension structure. The dynamics of the quarter suspension system were modelled based on Lagrange’s equation. In our work, the non-dominated sorting genetic algorithm III (NSGA-III) was selected for conducting multi-objective optimization of the suspension design, combined with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) to choose the optimal combination of parameters in the non-dominated solution set obtained by NSGA-III. We compared the performance of NSGA-III with that of other multi-objective evolutionary algorithms (MOEAs). Compared with the second-scoring solution, the score of the optimal solution obtained by NSGA-III increased by 4.92%, indicating that NSGA-III has a significant advantage in terms of the solution quality and robustness for the optimal design of the suspension system. This was verified by simulation in Adams that our method, which utilizes multibody dynamics, NSGA-III and TOPSIS, is feasible to determine the optimal design of a suspension system for an agricultural mobile robot.
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Yi-Hui Chen, Yi-Hui Chen, Heng-Zhou Ye Yi-Hui Chen, and Feng-Yi Huang Heng-Zhou Ye. "The Configuration Design of Electronic Products Based on improved NSGA-III with Information Feedback Models." 電腦學刊 33, no. 4 (August 2022): 081–94. http://dx.doi.org/10.53106/199115992022083304007.
Повний текст джерелаАнотація:
<p>The configuration of electronic products is an important means to meet the diverse and personalized needs of users and achieve mass customization, and one of its goals is to recommend an excellent bill of material to users according to users’ individualized needs and preferences. Current research describes the configuration of electronic products as a single-objective optimization model, which suffers from the problems of single recommended configuration and difficulty in meeting the dynamic adjustment of user preferences. Therefore, we describe it as a multi-objective optimization model and propose an NSGA-III-FR algorithm to solve the model. In order to balance the convergence and diversity of the algorithm, NSGA-III-FR has made two improvements on the basis of NSGA-III with information feedback models: introducing adaptive parameters to balance NSGA-III-F1 and NSGA-III-R1; and using Angle Penalized Distance (APD) to improve the niche technology. The experimental results show that our modified meth-od can achieve better performance compared with the other three algorithms.</p> <p> </p>
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Thawkar, Shankar, Law Kumar Singh, and Munish Khanna. "Multi-objective techniques for feature selection and classification in digital mammography." Intelligent Decision Technologies 15, no. 1 (March 24, 2021): 115–25. http://dx.doi.org/10.3233/idt-200049.
Повний текст джерелаАнотація:
Feature selection is a crucial stage in the design of a computer-aided classification system for breast cancer diagnosis. The main objective of the proposed research design is to discover the use of multi-objective particle swarm optimization (MOPSO) and Nondominated sorting genetic algorithm-III (NSGA-III) for feature selection in digital mammography. The Pareto-optimal fronts generated by MOPSO and NSGA-III for two conflicting objective functions are used to select optimal features. An artificial neural network (ANN) is used to compute the fitness of objective functions. The importance of features selected by MOPSO and NSGA-III are assessed using artificial neural networks. The experimental results show that MOPSO based optimization is superior to NSGA-III. MOPSO achieves high accuracy with a 55% feature reduction. MOPSO based feature selection and classification deliver an efficiency of 97.54% with 98.22% sensitivity, 96.82% specificity, 0.9508 Cohen’s kappa coefficient, and area under curve AZ= 0.983 ± 0.003.
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Trưởng, Nguyễn Huy, and Dinh-Nam Dao. "New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles." Advances in Mechanical Engineering 12, no. 2 (February 2020): 168781402090425. http://dx.doi.org/10.1177/1687814020904253.
Повний текст джерелаАнотація:
In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.
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Zhao, Yanlin. "Research on equipment layout of multi-layer circular manufacturing cell based on NSGA III." PLOS ONE 19, no. 12 (December 23, 2024): e0312364. https://doi.org/10.1371/journal.pone.0312364.
Повний текст джерелаАнотація:
This paper investigates the layout optimization of multi-layer circular manufacturing cells (MCMC), a topic that has garnered limited attention compared to single-layer circular manufacturing cells (SCMC). With the continuous advancement of global intelligent manufacturing, MCMC has emerged as a viable solution, with several smart factories already implementing this model. Existing literature predominantly utilizes the NSGA II algorithm for SCMC layouts due to their relatively few objectives. However, the layout problem for MCMC encompasses a significantly larger number of objectives, rendering NSGA II inadequate. This study aims to fill this research gap by proposing an innovative approach using NSGA III, specifically designed for complex multi-objective optimization. A multi-dimensional target mathematical model for MCMC is established, facilitating the systematic examination of layout configurations. The methodology employs NSGA III to effectively tackle the challenges posed by MCMC layouts. To validate the effectiveness of NSGA III, empirical data from a smart factory in Zhejiang, China, is utilized. The findings demonstrate that NSGA III significantly outperforms traditional algorithms, yielding superior solutions for MCMC layout problems. This research not only challenges the conventional SCMC layout paradigm but also expands the options available for facility layouts in smart factories. Ultimately, it addresses the pressing engineering needs of smart factory construction and contributes valuable insights to the field of MCMC research, establishing a robust methodology for future studies.
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Liu, Changrong, Hanqing Wang, Yifang Tang, and Zhiyong Wang. "Optimization of a Multi-Energy Complementary Distributed Energy System Based on Comparisons of Two Genetic Optimization Algorithms." Processes 9, no. 8 (August 10, 2021): 1388. http://dx.doi.org/10.3390/pr9081388.
Повний текст джерелаАнотація:
The development and utilization of low-carbon energy systems has become a hot topic of energy research in the international community. The construction of a multi-energy complementary distributed energy system (MCDES) is researched in this paper. Based on the multi-objective optimization theory, the planning optimization of an MCDES is studied, and a three-dimensional objective-optimization model is constructed by considering the constraints of the objective function and decision variables. Aiming at the optimization problem of building terminals for the MCDES studied in the paper, two genetic optimization algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Sorting Genetic Algorithm III (NSGA-III)—are used for calculation based on an example analysis. The constraint conditions of practical problems were added to the existing algorithms. Combined with the comparison of the solution quality and the optimal compromise solution of the two algorithms, a multi-decision method is proposed to obtain the optimal solution based on the Pareto optimal frontier of the two algorithms. Finally, the optimal decision scheme of the example is determined and the effectiveness and reliability of the optimization model are verified. Under the application of the MCDES optimization model studied in this paper, the iteration speed and hypervolume index of NSGA-III are found to be better than those of NSGA-II. The values of the life cycle cost and life cycle carbon emission objectives after the optimization of NSGA-III are indicated as 2% and 14% lower, respectively, than those of NSGA-II. The primary energy efficiency of NSGA-III is shown to be 20% higher than that of NSGA-II. According to the optimal decision, the energy operation strategies of the example MCDES with each typical day in the four seasons indicate that good integrated energy application and low-carbon operation performance are shown during the four-seasons operation process. The consumption of renewable energy is significant, which effectively reduces the application of high-grade energy. Thus, the theoretical guidance and engineering application reference are provided for MCDES design planning and operation optimization.
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Dao, Dinh-Nam, and Li-Xin Guo. "New hybrid NSGA-III&SPEA/R to multi-object optimization in a half-car dynamic model." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 6 (December 24, 2019): 1660–71. http://dx.doi.org/10.1177/0954407019890164.
Повний текст джерелаАнотація:
In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic Algorithm II (NSGA-III) and SPEA/R (HNSGA-III&SPEA/R). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R and NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.
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Harold, Victor Jinn, Ugochukwu Nwosu, and Matthew Uzoma Shadrach. "Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method." Advances in Multidisciplinary and scientific Research Journal Publication 11, no. 4 (December 10, 2023): 45–70. http://dx.doi.org/10.22624/aims/maths/v11n4p4.
Повний текст джерелаАнотація:
For a generic closed-loop supply chain network (CLSCN), a mixed-integer Linear programing (MILP) Model was developed that simultaneously optimizes three of the fundamental elements of sustainability: cost, social impact, and environmental effect. Many real-world features of SC operations, which includes multiple products types, multiple echelons, multiple production technologies, and multiple modes of transportation, were all incorporated into the model during its development. As a result of the complexity of the model and need to provide Pareto front for decision maker, a novel hybrid NSGA III (HNSGA-III) was developed to solve the model. The Relative Gap was used result of the model was validated using the ε-constraint method. the algorithm parameters were tuned using Taguchi design then the performance of HNSGA III as compared to NSGA III, HypE and MOEA/D was assessed using RNS, SNS, IGD, CPU times and HC index. The result clearly shows the HNSGA III outperforms the other meta-heuristic algorithms. Keywords: Closed loop supply chain network, Sustainability, CO2 Emission, Multi-objective model, HNSGA III, NSGA III, MOEA/D, HypE Victor Jinn, Harold Ugochukwu Nwosu & Matthew Uzoma Shadrach (2023): Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 45-70. dx.doi.org/10.22624/AIMS/MATHS/V11N4P4. Available online at www.isteams.net/mathematics-computationaljournal.
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Yang, Shiting, Helong Shen, Zhenyang Zhong, Xiaobin Qian, and Yufei Wang. "Collaborative Scheduling for Yangtze Riverport Channels and Berths Using Multi-Objective Optimization." Applied Sciences 14, no. 15 (July 25, 2024): 6514. http://dx.doi.org/10.3390/app14156514.
Повний текст джерелаАнотація:
Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model’s efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities.
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Yannibelli, Virginia, Elina Pacini, David A. Monge, Cristian Mateos, Guillermo Rodriguez, Emmanuel Millán, and Jorge R. Santos. "An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds." Scientific Programming 2023 (February 17, 2023): 1–26. http://dx.doi.org/10.1155/2023/8345646.
Повний текст джерелаАнотація:
Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances of different VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends significantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral differences with NSGA-III. Then, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering different application sizes. To do that, we use the well-known CloudSim simulator and consider different VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and significant savings in terms of computing time (10%–17%), monetary cost (10%–40%), and spot instance interruptions (33%–100%).
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Wang, Chun, Zhicheng Ji, and Yan Wang. "Many-objective flexible job shop scheduling using NSGA-III combined with multi-attribute decision making." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840110. http://dx.doi.org/10.1142/s0217984918401103.
Повний текст джерелаАнотація:
This paper considers many-objective flexible job shop scheduling problem (MaOFJSP) in which the number of optimization problems is larger than three. An integrated multi-objective optimization method is proposed which contains both optimization and decision making. The non-dominated sorting genetic algorithm III (NSGA-III) is utilized to find a trade-off solution set by simultaneously optimizing six objectives including makespan, workload balance, mean of earliness and tardiness, cost, quality, and energy consumption. Then, an integrated multi-attribute decision-making method is introduced to select one solution that fits into the decision maker’s preference. NSGA-III is compared with three multi-objective evolutionary algorithms (MOEAs)-based scheduling methods, and the simulation results show that NSGA-III performs better in generating the Pareto solutions. In addition, the impacts of using different reference points and decoding methods are investigated.
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Moshref, Mahmoud, Rizik Al-Sayyed, and Saleh Al-Sharaeh. "Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 1182. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1182-1196.
Повний текст джерелаАнотація:
<span>Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field for researchers because these algorithms improve two or more conflicting objectives and present the best trade-off between the conflicting objectives to solve multi-objective problems (MOPs). Previous research proposed an algorithm that relies on non-dominated sorting genetic algorithm 3 (NSGA-III), namely enhanced non-dominated sorting genetic routing algorithm (ENSGRA). This algorithm is used to optimise three objectives, which include number of worked sensors, energy consuming and node covering area. The fourth objective, which is node load balancing, is added in the current research. Such an addition aims to improve node distribution around cluster heads and decrease network congestion, thus decreasing energy consumption and increasing network lifetime. The ENSGRA algorithm is compared with multi-objective multi-step particle swarm optimisation (MOMSPSO), non-dominated sorting genetic algorithm 2 (NSGA-II), and NSGA-III. The proposed algorithm ENSGRA exceed to MOMSPSO, NSGA-II, and NSGA-III in the proposed QoS model in the final outcomes, as the proposed approach achieved (38%) average combination (optimisation) percentage. Which is the highest percentage over other methods.</span>
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Mkaouer, Wiem, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb, and Ali Ouni. "Many-Objective Software Remodularization Using NSGA-III." ACM Transactions on Software Engineering and Methodology 24, no. 3 (May 13, 2015): 1–45. http://dx.doi.org/10.1145/2729974.
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Li, Yaxue, Hongzhi Xie, Xiaolin Deng, Jin Zhang, Shuhui Liu, and Li Wang. "Inventory optimisation based on NSGA-III algorithm." International Journal of Space-Based and Situated Computing 9, no. 3 (2023): 158–64. http://dx.doi.org/10.1504/ijssc.2023.133247.
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WANG, Li, and Wei WANG. "Hyperspectral image compressed processing: Evolutionary multi-objective optimization sparse decomposition." PLOS ONE 17, no. 4 (April 29, 2022): e0267754. http://dx.doi.org/10.1371/journal.pone.0267754.
Повний текст джерелаАнотація:
In the compressed processing of hyperspectral images, orthogonal matching pursuit algorithm (OMP) can be used to obtain sparse decomposition results. Aimed at the time-complex and difficulty in applying real-time processing, an evolutionary multi-objective optimization sparse decomposition algorithm for hyperspectral images is proposed. Instead of using OMP for the matching process to search optimal atoms, the proposed algorithm explores the idea of reference point non-dominated sorting genetic algorithm (NSGA) to optimize the matching process of OMP. Take two objective function to establish the multi-objective sparse decomposition optimization model, including the largest inner product of matching atoms and image residuals, and the smallest correlation between atoms. Utilize NSGA-III with advantage of high accuracy to solve the optimization model, and the implementation process of NSGA-III-OMP is presented. The experimental results and analysis carried on four hyperspectral datasets demonstrate that, compared with the state-of-the-art algorithms, the proposed NSGA-III-OMP algorithm has effectively improved the sparse decomposition performance and efficiency, and can effectively solve the sparse decomposition optimization problem of hyperspectral images.
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Zhong, Jialin, Yahui Wu, Wubin Ma, Su Deng, and Haohao Zhou. "Optimizing Multi-Objective Federated Learning on Non-IID Data with Improved NSGA-III and Hierarchical Clustering." Symmetry 14, no. 5 (May 23, 2022): 1070. http://dx.doi.org/10.3390/sym14051070.
Повний текст джерелаАнотація:
Federated learning (FL) can tackle the problem of data silos of asymmetric information and privacy leakage; however, it still has shortcomings, such as data heterogeneity, high communication cost and uneven distribution of performance. To overcome these issues and achieve parameter optimization of FL on non-Independent Identically Distributed (non-IID) data, a multi-objective FL parameter optimization method based on hierarchical clustering and the third-generation non-dominated sorted genetic algorithm III (NSGA-III) algorithm is proposed, which aims to simultaneously minimize the global model error rate, global model accuracy distribution variance and communication cost. The introduction of a hierarchical clustering algorithm on non-IID data can accelerate convergence so that FL can employ an evolutionary algorithm with a low FL client participation ratio, reducing the overall communication cost of the NSGA-III algorithm. Meanwhile, the NSGA-III algorithm, with fast greedy initialization and a strategy of discarding low-quality individuals (named NSGA-III-FD), is proposed to improve the convergence efficiency and the quality of Pareto-optimal solutions. Under two non-IID data settings, the CNN experiments on both MNIST and CIFAR-10 datasets show that our approach can obtain better Pareto-optimal solutions than classical evolutionary algorithms, and the selected solutions with an optimized model can achieve better multi-objective equilibrium than the standard federated averaging (FedAvg) algorithm and the Clustering-based FedAvg algorithm.
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Gao, Yanning, Xiaowen Shi, Haozhe Zhang, and Renwu Tang. "Optimized Resource Allocation for Sustainable Development in Beijing: Integrating Water, Land, Energy, and Carbon Nexus." Land 13, no. 10 (October 21, 2024): 1723. http://dx.doi.org/10.3390/land13101723.
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Chinese megacities face significant challenges in reducing carbon emissions while balancing economic growth and social welfare. This study constructs an innovative multi-objective optimization model, the SD-NSGA-III model, integrated with a System Dynamics (SD) model and using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize resource allocation in Beijing. The model targets environmental, economic, and social goals by establishing a water–land–energy–carbon (WLEC) nexus for analyzing resource allocation strategies and carbon reduction pathways under various constraints. Scenario simulations under the efficiency-oriented scenario indicated a potential reduction in energy carbon emissions of 81.4% by 2030. The fairness-oriented scenario revealed significant trade-offs between equity and emission reductions, emphasizing the need for balanced strategies. Introducing constraints on resources and economic growth significantly reduced median energy carbon emissions to 80 million tons by 2030. These findings demonstrate the effectiveness of the SD-NSGA-III model in providing actionable strategies for achieving carbon neutrality and sustainable development in cities.
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Zhang, Zhengrui, Fei Wu, and Aonan Wu. "Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm." Processes 12, no. 5 (May 7, 2024): 950. http://dx.doi.org/10.3390/pr12050950.
Повний текст джерелаАнотація:
To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.
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Zhang, Haijuan, Gai-Ge Wang, Junyu Dong, and Amir H. Gandomi. "Improved NSGA-III with Second-Order Difference Random Strategy for Dynamic Multi-Objective Optimization." Processes 9, no. 6 (May 21, 2021): 911. http://dx.doi.org/10.3390/pr9060911.
Повний текст джерелаАнотація:
Most real-world problems that have two or three objectives are dynamic, and the environment of the problems may change as time goes on. For the purpose of solving dynamic multi-objective problems better, two proposed strategies (second-order difference strategy and random strategy) were incorporated with NSGA-III, namely SDNSGA-III. When the environment changes in SDNSGA-III, the second-order difference strategy and random strategy are first used to improve the individuals in the next generation population, then NSGA-III is employed to optimize the individuals to obtain optimal solutions. Our experiments were conducted with two primary objectives. The first was to test the values of the metrics mean inverted generational distance (MIGD), mean generational distance (MGD), and mean hyper volume (MHV) on the test functions (Fun1 to Fun6) via the proposed algorithm and the four state-of-the-art algorithms. The second aim was to compare the metrics’ value of NSGA-III with single strategy and SDNSGA-III, proving the efficiency of the two strategies in SDNSGA-III. The comparative data obtained from the experiments demonstrate that SDNSGA-III has good convergence and diversity compared with four other evolutionary algorithms. What is more, the efficiency of second-order difference strategy and random strategy was also analyzed in this paper.
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Mahmoud, Ali, and Xiaohui Yuan. "SHAPE OPTIMIZATION OF ROCKFILL DAM WITH RUBIK CUBE REPRODUCTION BASED MULTI-OBJECTIVE PARTICLE SWARM ALGORITHM." ASEAN Engineering Journal 11, no. 4 (November 25, 2021): 204–31. http://dx.doi.org/10.11113/aej.v11.18021.
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A rockfill dam's quality and its economic aspects are inextricably interwoven with each other. Approaching the optimal design of a rockfill dam paves the path to achieve the best quality with the fewest expenses. Choosing the Sardasht rockfill dam as a case study, two semi-empirical models are presented for seepage and safety factor. These two models, together with construction costs, were employed as three objective functions for the Sardasht rockfill dam's shape optimization. Optimization was handled using a robust multi-objective particle swarm optimization algorithm (RCR-MOPSO). A new reproducing method inspired by a Rubik's cube shape (RCR) and NSGA-III are building blocks of RCR-MOPSO. Three benchmark problems and two real-world problems were solved using RCR-MOPSO and compared with NSGA-III and MOPSO to ensure the performance of RCR-MOPSO. The solution quality and performance of RCR-MOPSO are significantly better than the original MOPSO and close to NSGA-III. Nevertheless, RCR-MOPSO recorded a 38% shorter runtime than NSGA-III. RCR-MOPSO presented a set of non-dominated solutions as final results for the Sardasht rockfill dam shape optimization. Due to the defined constraints, all solutions dominate the original design. Regarding the final results, compared with Sardasht dam's original design, the construction price was reduced by 31.12% on average, while seepage and safety factor improved by 15.84% and 27.78% on average, respectively.
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Mociran, Bogdan, and Marian Gliga. "Optimization of an Inductive Displacement Transducer." Sensors 23, no. 19 (September 28, 2023): 8152. http://dx.doi.org/10.3390/s23198152.
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This paper presents the optimization of an inductive displacement transducer or linear variable differential transformer (LVDT). The method integrates design software (SolidWorks 2023), simulation tools (COMSOL Multiphysics), and MATLAB. The optimization phase utilizes the non-dominated sorting genetic algorithm (NSGA)-II and -III to fine-tune the geometry configuration by adjusting six inner parameters corresponding to the dimension of the interior components of the LVDT, thus aiming to improve the overall performance of the device. The outcomes of this study reveal a significant achievement in LVDT enhancement. By employing the proposed methodology, the operational range of the LVDT was effectively doubled, extending it from its initial 8 (mm) to 16 (mm). This expansion in the operational range was achieved without compromising measurement accuracy, as all error values for the working range of 0–16 (mm) (NSGA-II with a maximum final relative error of 2.22% and NSGA-III with 2.44%) remained below the imposed 3% limit. This research introduces a new concept in LVDT optimization, capitalizing on the combined power of NSGA-II and NSGA-III algorithms. The integration of these advanced algorithms, along with the interconnection between design, simulation, and programming tools, distinguishes this work from conventional approaches. This study fulfilled its initial objectives and generated quantifiable results. It introduced novel internal configurations that substantially improved the LVDT’s performance. These achievements underscore the validity and potential of the proposed methodology in advancing LVDT technology, with promising implications for a wide range of engineering applications.
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Xin, Shijin, Guojie Hao, Pan Zhang, Changhui Fan, Haitao Niu, and Zhaoxu Zhang. "Optimization scheduling of power flow pre allocation for flexible interconnection of distribution networks using improved Non-dominated Sorting Genetic Algorithm III." Journal of Physics: Conference Series 2903, no. 1 (November 1, 2024): 012027. https://doi.org/10.1088/1742-6596/2903/1/012027.
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Abstract In order to stabilize and effectively control the operation status of the distribution network, we can take a series of measures to ensure the stable operation of the system. An improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm is proposed to optimize the pre-distribution of flexible interconnected power flow in the distribution network. Based on the improved NSGA-III algorithm, an optimal regulation framework of flexible interconnected power flow in the distribution network is constructed. According to this framework, the minimum loss objective function and peak load shedding objective function are calculated, and the constraint conditions are set according to the constraint conditions of these two functions. By using the improved NSGA-III algorithm, the objective function of optimal regulation of flexible interconnected power flow in the distribution network is solved. The experimental results show that this method is remarkable in frequency stability, with the smallest fluctuation amplitude, and can quickly keep the frequency stable. After applying this method, the voltage amplitude of phase A is effectively adjusted.
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Chen, Weiye, Zehua Hu, Xuechao Gao, and Yefei Liu. "Simulation and Multi-Objective Optimization of Three-Column Double-Effect Methanol Distillation by NSGA-III Algorithm." Processes 11, no. 5 (May 16, 2023): 1515. http://dx.doi.org/10.3390/pr11051515.
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The multi-objective optimization of methanol distillation is a critical and complex issue in the methanol industry. The three-column methanol distillation scheme is first simulated with Aspen Plus to provide the initial value of the NSGA-III algorithm. The operating parameters are optimized through the Python-Aspen platform. The total annual cost and CO2 emissions are considered the objective function. A small value of indicator generational distance can be achieved by increasing the number of generations, which is helpful in improving algorithm convergence. The NSGA-III algorithm has good convergence and distribution performance. By comparing the optimized results with the original ones, the total annual cost and CO2 emissions are, respectively, reduced by 5.35% and 12.80% when the operating parameters of the methanol distillation sequence are optimized through NSGA-III. As a result, substantial economic and energy savings can be made, offering great potential to improve the performance of the three-column methanol distillation.
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Liu, Jiajia, Edi Syams Zainudin, Azizan As'arry, and Mohd Idris Shah Ismail. "Modeling Approach of Cloud 4D Printing Service Composition Optimization Based on Non-Dominated Sorting Genetic Algorithm III." International Journal of Automotive and Mechanical Engineering 21, no. 3 (September 20, 2024): 11453–68. http://dx.doi.org/10.15282/ijame.21.3.2024.1.0884.
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The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance (GD) and inverted generational distance (IGD), particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.
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Cui, Zhihua, Yu Chang, Jiangjiang Zhang, Xingjuan Cai, and Wensheng Zhang. "Improved NSGA-III with selection-and-elimination operator." Swarm and Evolutionary Computation 49 (September 2019): 23–33. http://dx.doi.org/10.1016/j.swevo.2019.05.011.
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Xue, Xingsi, Jiawei Lu, and Junfeng Chen. "Using NSGA‐III for optimising biomedical ontology alignment." CAAI Transactions on Intelligence Technology 4, no. 3 (June 3, 2019): 135–41. http://dx.doi.org/10.1049/trit.2019.0014.
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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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Gan, Wei, and Bo Wang. "Research on Intelligent Control Algorithm of Complex Borehole Trajectory Based on Multi-objective Optimization." Academic Journal of Science and Technology 13, no. 2 (November 29, 2024): 215–20. http://dx.doi.org/10.54097/69rw7813.
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Effective wellbore trajectory control is essential in complex drilling environments, where precision and operational efficiency are critical to reducing costs and improving safety. Traditional trajectory control methods often face limitations in addressing multiple conflicting objectives, such as minimizing trajectory deviation while maximizing drilling efficiency. Traditional trajectory control methods often face limitations in addressing multiple conflicting objectives, such as minimizing trajectory deviation while maximizing drilling efficiency. This study presents an intelligent control approach utilizing the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective optimization in complex wellbore trajectory control. designing and implementing a set of objective functions tailored to trajectory control requirements, this approach leverages NSGA-III' s ability to handle high-dimensional trajectories. s ability to handle high-dimensional objective spaces, achieving a balanced optimization across diverse performance metrics. Experimental results Experimental results verify the effectiveness of this approach, with Matlab simulations demonstrating significant improvements in trajectory accuracy and computational efficiency. This research provides a robust framework for multi-objective trajectory control and highlights the potential of NSGA-III in enhancing decision-making for complex drilling applications. decision-making for complex drilling applications.
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Chaudhari, Pranava, Amit K. Thakur, Rahul Kumar, Nilanjana Banerjee, and Amit Kumar. "Comparison of NSGA-III with NSGA-II for multi objective optimization of adiabatic styrene reactor." Materials Today: Proceedings 57 (2022): 1509–14. http://dx.doi.org/10.1016/j.matpr.2021.12.047.
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Yadav, Rishika, and Raghuraj Singh. "A multi objective optimization in wireless sensor network using enhanced NSGA-3 algorithm." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 7 (2023): 1925–37. http://dx.doi.org/10.47974/jdmsc-1727.
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Wireless sensor network (WSN) is one of the most challenging study areas in the field of information technology. Among the many challenges posed by WSN, optimization in the presence of multiple conflicting objectives is one of the most major challenges. This paper discusses the previous research work in the field of MOO WSN. This research proposes a new algorithm, Enhanced NSGA-III which is a reference based with new dynamic weight-based cluster scheduling algorithm. ENSGA uses multi-parent order crossover (MPOX) to enhance fresh child to produce the best Pareto Fronts (PF). Total number of sensor nodes, network coverage and energy consumption are considered as the three conflicting objectives for the optimization study. ENSGA exhibits promising results in comparison to the NSGA-II and NSGA-III. The possible future work is also discussed in the paper.
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Wang, Yanjiao, and Xiaonan Sun. "A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment." Computational Intelligence and Neuroscience 2018 (October 22, 2018): 1–21. http://dx.doi.org/10.1155/2018/4527968.
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In order to improve the convergence and distribution of a many-objective evolutionary algorithm, this paper proposes an improved NSGA-III algorithm based on weight vector adjustment (called NSGA-III-WA). First, an adaptive weight vector adjustment strategy is proposed to decompose the objective space into several subspaces. According to different subspace densities, the weight vector is sparse or densely adjusted to ensure the uniformity of the weight vector distribution on the Pareto front surface. Secondly, the evolutionary model that combines the new differential evolution strategy and genetic evolution strategy is proposed to generate new individuals and enhance the exploration ability of the weight vector in each subspace. The proposed algorithm is tested on the optimization problem of 3–15 objectives on the DTLZ standard test set and WFG test instances, and it is compared with the five algorithms with better effect. In this paper, the Whitney–Wilcoxon rank-sum test is used to test the significance of the algorithm. The experimental results show that NSGA-III-WA has a good effect in terms of convergence and distribution.
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Tong, Zheming, Jiage Xin, and Chengzhen Ling. "Many-Objective Hybrid Optimization Method for Impeller Profile Design of Low Specific Speed Centrifugal Pump in District Energy Systems." Sustainability 13, no. 19 (September 23, 2021): 10537. http://dx.doi.org/10.3390/su131910537.
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Low specific speed centrifugal pumps (LSSCP) are widely utilized in district energy systems to promote the integration of renewable energy. However, the performance of LSSCP becomes inefficient due to harsh operating conditions resulting in substantial increase in energy consumption. Many-objective optimization is significant in improving the performance of LSSCP and promoting the sustainability of district energy systems. Among the existing optimization methods, global optimization methods are limited by high computational cost when solving many-objective optimization problems, and gradient-based optimization methods face difficulties in locating the global optimum. In the present study, a hybrid optimization method was developed for solving many-objective optimization problems of LSSCP. The LSSCP optimization result of the hybrid algorithm was compared with that of the non-dominated sorting genetic algorithm (NSGA), so as to demonstrate the capacity of the proposed method. In the designed flow condition without cavitation, the hydraulic efficiency obtained by the hybrid optimization algorithm was found to be 9.5%, 5.4%, and 4.7% higher than those of the original, NSGA-II, and NSGA-III optimized results, respectively. The shaft power was 10.3%, 8.7% and 5.1% less than said three optimized results. The maximum turbulent kinetic energy in the flow passage obtained from the hybrid optimization was only 2.2 J/kg, which was 67% and 46% less than that of the NSGA-II and NSGA-III optimized results, respectively. In the designed flow condition with cavitation, the net positive suction head critical optimized by the hybrid model was 0.857 m, which was substantially reduced compared with the original and NSGA- II optimized results.
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Yannibelli, Virginia, Elina Pacini, David Monge, Cristian Mateos, and Guillermo Rodriguez. "A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud." Scientific Programming 2020 (August 28, 2020): 1–17. http://dx.doi.org/10.1155/2020/4653204.
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The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.
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Maitre, Julien, Sébastien Gaboury, Bruno Bouchard, and Abdenour Bouzouane. "A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms." International Journal of Monitoring and Surveillance Technologies Research 3, no. 3 (July 2015): 44–67. http://dx.doi.org/10.4018/ijmstr.2015070103.
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Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.
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Mao, Huihui, Chen Wang, Yan He, Xianfeng Song, Run Ma, Runkui Li, and Zheng Duan. "Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization." Water 16, no. 21 (October 22, 2024): 3030. http://dx.doi.org/10.3390/w16213030.
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In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.
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Hu, Qiong, Xiaoyu Zhai, and Zhenfu Li. "Multi-Objective Optimization of Deep-Sea Mining Pump Based on CFD, GABP Neural Network and NSGA-III Algorithm." Journal of Marine Science and Engineering 10, no. 8 (August 2, 2022): 1063. http://dx.doi.org/10.3390/jmse10081063.
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In order to improve the hydraulic performance of a deep-sea mining pump, this research proposed a multi-objective optimization strategy based on the computational fluid dynamics (CFD) numerical simulation, genetic algorithm back propagation (GABP) neural network, and non-dominated sorting genetic algorithm-III (NSGA-III). Significance analysis of the impeller and diffuser parameters was conducted using the Plackett–Burman experiment to filter out the design variables. The optimum Latin hypercube sampling method was used to produce sixty sample cases. The GABP neural network was then utilized to establish an approximate model between the pump’s hydraulic performance and design variables. Finally, the NSGA-III was utilized to solve the approximation model to determine the optimum parameters for the impeller and diffuser. The results demonstrate that the GABP neural network can accurately forecast the deep-sea mining pump’s hydraulic performance, and the NSGA-III global optimization is effective. On the rated clear water conditions, the optimized pump has a 14.65% decrease in shaft power and a 6.04% increase in efficiency while still meeting the design requirements for the head. Under rated solid-liquid two-phase flow conditions, the head still meets the design requirements, the shaft power is decreased by 15.64%, and the efficiency is increased by 6.00%.
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Sicuaio, Tomé, Pengxiang Zhao, Petter Pilesjo, Andrey Shindyapin, and Ali Mansourian. "Sustainable and Resilient Land Use Planning: A Multi-Objective Optimization Approach." ISPRS International Journal of Geo-Information 13, no. 3 (March 18, 2024): 99. http://dx.doi.org/10.3390/ijgi13030099.
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Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience.
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Chan, Felix T. S., Zhengxu Wang, Yashveer Singh, X. P. Wang, J. H. Ruan, and M. K. Tiwari. "Activity scheduling and resource allocation with uncertainties and learning in activities." Industrial Management & Data Systems 119, no. 6 (July 8, 2019): 1289–320. http://dx.doi.org/10.1108/imds-01-2019-0002.
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Purpose The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also considers learning rate and uncertainties in the activity durations. Design/methodology/approach An activity schedule with requirements of different resource units is used to calculate the objectives: makespan and resource efficiency. A comparisons between non-dominated sorting genetic algorithm – II (NSGA-II) and non-dominated sorting genetic algorithm – III (NSGA-III) is done to calculate near optimal solutions. Buffers are introduced in the activity schedule to take uncertainty into account and learning rate is used to incorporate the learning effect. Findings The results show that NSGA-III gives better near optimal solutions than NSGA-II for multi-objective problem with different complexities of activity schedule. Research limitations/implications The paper does not considers activity sequencing with multiple activity relations (for instance partial overlapping among different activities) and dynamic events occurring in between or during activities. Practical implications The paper helps project managers in manufacturing industry to schedule the activities and allocate resources for a near-real world environment. Originality/value This paper takes into account both the learning rate and the uncertainties in the activity duration for a resource constrained project management problem. The uncertainty in both the individual durations of activities and the whole project duration time is taken into consideration. Genetic algorithms were used to solve the problem at hand.
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Wu, Yun, Du Yan, Jie-Ming Yang, An-Ping Wang, and Dan Feng. "Optimal scheduling strategy of electric vehicle based on improved NSGA-III algorithm." PLOS ONE 19, no. 5 (May 17, 2024): e0298572. http://dx.doi.org/10.1371/journal.pone.0298572.
Повний текст джерелаАнотація:
Aiming at the problem of load increase in distribution network and low satisfaction of vehicle owners caused by disorderly charging of electric vehicles, an optimal scheduling model of electric vehicles considering the comprehensive satisfaction of vehicle owners is proposed. In this model, the dynamic electricity price and charging and discharging state of electric vehicles are taken as decision variables, and the income of electric vehicle charging stations, the comprehensive satisfaction of vehicle owners considering economic benefits and the load fluctuation of electric vehicles are taken as optimization objectives. The improved NSGA-III algorithm (DJM-NSGA-III) based on dynamic opposition-based learning strategy, Jaya algorithm and Manhattan distance is used to solve the problems of low initial population quality, easy to fall into local optimal solution and ignoring potential optimal solution when NSGA-III algorithm is used to solve the multi-objective and high-dimensional scheduling model. The experimental results show that the proposed method can improve the owner’s satisfaction while improving the income of the charging station, effectively alleviate the conflict of interest between the two, and maintain the safe and stable operation of the distribution network.
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Alshammari, Nahar F., Mohamed Mahmoud Samy, and Shimaa Barakat. "Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems." Mathematics 11, no. 7 (April 5, 2023): 1741. http://dx.doi.org/10.3390/math11071741.
Повний текст джерелаАнотація:
This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations.
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Mohamed, Marwa F., Mohamed Meselhy Eltoukhy, Khalil Al Ruqeishi, and Ahmad Salah. "An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision." Mathematics 11, no. 6 (March 22, 2023): 1537. http://dx.doi.org/10.3390/math11061537.
Повний текст джерелаАнотація:
With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.
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Hernandez, Concepcion, Jorge Lara, Marco A. Arjona, and Enrique Melgoza-Vazquez. "Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm." Energies 16, no. 5 (February 26, 2023): 2248. http://dx.doi.org/10.3390/en16052248.
Повний текст джерелаАнотація:
This paper presents a multi-objective design optimization of a power transformer to find the optimal geometry of its core and the low- and high-voltage windings, representing the minimum power losses and the minimum core and copper weights. The optimal design is important because it allows manufacturers to build more efficient and economical transformers. The approach employs a manufacturer’s design methodology, which is based on the usage of the laws of physics and leads to an analytical transformer model with the advantage of requiring a low amount of computing time. Afterward, the multi-objective design optimization is defined along with its constraints, and they are solved using the Non-Sorting Genetic Algorithm III (NSGA-III), which finds a set of optimal solutions. Once an optimal solution is selected from the Pareto front, it is necessary to fine-tune it with the 3D Finite Element Analysis (FEA). To avoid the large computing times needed to carry out the 3D Finite Element (FE) model simulations used in multi-objective design optimization, Response Surface Methodology (RSM) polynomial models are developed using 3D FE model transformer simulations. Finally, a second multi-objective design optimization is carried out using the developed RSM empirical models that represent the cost functions and is solved using the NSGA-III. The numerical results of the optimal core and windings geometries demonstrate the validity of the proposed design methodology based on the NSGA-III. The used global optimizer has the feature of solving optimization problems with many cost functions, but it has not been applied to the design of transformers. The results obtained in this paper demonstrate better performance and accuracy with respect to the commonly used NSGA-II.
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Mohamed, Marwa F., Mai Dahshan, Kenli Li, and Ahmad Salah. "Virtual Machine Replica Placement Using a Multiobjective Genetic Algorithm." International Journal of Intelligent Systems 2023 (June 28, 2023): 1–16. http://dx.doi.org/10.1155/2023/8378850.
Повний текст джерелаАнотація:
Virtual machine (VM) replication is a critical task in any cloud computing platform to ensure the availability of the cloud service for the end user. In this task, one primary VM resides on a physical machine (PM) and one or more replicas reside on separate PMs. In cloud computing, VM placement (VMP) is a well-studied problem in terms of different goals, such as power consumption reduction. The VMP problem can be solved by using heuristics, namely, first-fit and meta-heuristics such as the genetic algorithm. Despite extensive research into the VMP problem, there are few works that consider VM replication when choosing a VMP. In this context, we proposed studying the problem of optimal VMP considering VM replication requirements. The proposed work frames the problem at hand as a multiobjective problem and adapts a nondominated sorting genetic algorithm (NSGA-III) to address the problem. VM replicas’ placement should consider several dimensions such as the geographical distance between the PM hosting the primary VM and the other PMs hosting the replicas. In addition, to this end, the proposed model aims to minimize (1) power consumption, (2) performance degradation, and (3) the distance between the PMs hosting the primary VM and its replica(s). The proposed method is thoroughly tested on a variety of computing environments with various heterogeneous VMs and PMs, including compute-intensive and memory-intensive environments. The obtained results illustrate the performance disparity between the adapted NSGA-III and MOEA/D methods and other methods of comparison, including heuristic and meta-heuristic approaches, with NSGA-III outperforming other comparison methods. For instance, in memory-intensive and in heterogeneous environments, the NSGA-III method’s performance was superior to the first-fit, next-fit, best-fit, PSO, and MOEA/D methods by 58%, 62%, 64%, 55%, and 31%, respectively.
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Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-III." International Journal of Decision Support System Technology 14, no. 1 (January 1, 2022): 1–28. http://dx.doi.org/10.4018/ijdsst.311066.
Повний текст джерелаАнотація:
Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..