Academic literature on the topic 'Multiple Objective Genetic Algorithm (MOGA)'
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Journal articles on the topic "Multiple Objective Genetic Algorithm (MOGA)"
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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.
Dissertations / Theses on the topic "Multiple Objective Genetic Algorithm (MOGA)"
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Furuhashi, Takeshi, Tomohiro Yoshikawa, and Masafumi Yamamoto. "A Study on Effects of Migration in MOGA with Island Model by Visualization." 日本知能情報ファジィ学会, 2008. http://hdl.handle.net/2237/20680.
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Session ID: SA-G4-2Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, September 17-21, 2008, Nagoya University, Nagoya, Japan
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Dinh, Duy Cuong. "Development of a Detailed Approach to Model the Solid Pyrolysis with the Coupling Between Solid and Gases Intra-Pores Phenomena." Electronic Thesis or Diss., Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2024. http://www.theses.fr/2024ESMA0029.
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La pyrolyse du bois est un processus crucial dans la science de la sécurité incendie car elle affecte la décomposition thermique et le comportement de combustion des matériaux. Le bois est un composite biopolymères (cellulose, hémicellulose et lignine) qui subit une pyrolyse complexe, produisant du charbon solide, du goudron et des gaz. Le processus de pyrolyse modifie également certaines caractéristiques importantes de l’échantillon (densité, conductivité thermique, capacité thermique, porosité, perméabilité, émissivité...) qui évoluent tout au long de la réaction de décomposition. La compréhension de ces transformations est cruciale pour la modélisation du comportement du feu des solides. Les évolutions des masses normalisées finales entre les expériences ATG et en cône calorimètre remettent en cause les modèles de taux de réaction solides existants. Les modèles actuels supposent souvent un ordre de réaction égal à 1, ce qui conduit à des inexactitudes lorsque l’ordre de réaction diffère de 1. Pour surmonter ces lacunes, un nouveau modèle basé sur la conversion, appelé ”Masse Initiale Virtuelle”, est proposé. Ce modèle est basé sur des données issues d’essais ATG. Il calcule la vitesse de chaque réaction dans le cas de mécanismes de pyrolyse complexes, avec de nombreuses réactions séquentielles et compétitives et a été implémenté en C++. Le code C++ de ce modèle est intégré avec l’outil DAKOTA pour permettre l’optimisation multi-objectif par algorithme génétique (MOGA) des paramètres cinétiques sur plusieurs vitesses de chauffage. Ce modèle de « Masse Initiale Virtuelle » est intégré dans la boîte à outils d’analyse des matériaux poreux basée sur OpenFOAM (PATO), un outil Open Source créé par la NASA. D’autres modèles de transferts de masse, de chaleur et de conservation des espèces en plus des propriétés des matériaux sont créés dans ce nouveau cadre. Un modèle informatique pour les réactions secondaires (réactions en phase gazeuse qui produisent du charbon secondaire) est implémenté dans PATO. Les simulations des essais en cône calorimètre sont effectuées dans des modèles 1D et 2D axisymétriques pour explorer l’influence des propriétés anisotropes du bois, en particulier l’orientation de ses fibres. La comparaison des modèles avec et sans réactions secondaires démontre le rôle de ces dernières dans la distribution de la chaleur et la production de charbon secondaire. Ce résultat explique la différence de masse finale observée expérimentalement entre les tests en ATG et en cône calorimètre. La comparaison des résultats expérimentaux et numériques montre la pertinence de cette approche pour simuler le comportement complexe de la pyrolyse du bois en mettant en évidence l’importance des voies de réaction, des réactions secondaires, du transfert de chaleur, du transfert de masse et des phénomènes d’interaction intra-porePyrolysis of wood is a crucial process in fire safety science because it affects the thermal decomposition and combustion behavior of materials. Wood, a composite of biopolymeric components (cellulose, hemicellulose and lignin) undergoes complex pyrolysis to yield solid char, tar and gases as it thermally decomposes. The pyrolysis process also changes some important characteristics of the sample (density, thermal conductivity, heat capacity, porosity, permeability, emissivity...) that evolve throughout the reaction. Understanding these transformations is crucial for the correct modeling of fire behavior and material response under different thermal conditions. Different final normalized mass between TGA and cone calorimeter experiments challenge existing solid reaction rate models, according to experimental studies. Current models often assume a reaction order of 1, which oversimplifies the complexity of wood pyrolysis and leads to inaccuracies when the reaction order differs from 1. To overcome these shortcomings, a brand new conversion-based model, called ”Virtual Initial Mass”, is proposed. This model, based on TGA data, calculates the reaction rate for each reaction in complicated pyrolysis mechanisms. It supports mechanisms with numerous sequential and competitive reactions and has been implemented in C++. The C++ code for this model is integrated with the DAKOTA toolkit to perform multi objective genetic algorithm (MOGA) optimization of kinetic parameters for multiple heating rates. This ”Virtual Initial Mass” model is integrated in the Porous material Analysis Toolbox based on OpenFOAM (PATO) an Open Source tool distributed by NASA. Further mass transfer, heat transfer, species conservation models in addition to material properties are created within this new framework. A computational model for secondary reactions (gas-phase reactions that produce secondary char) is implemented in PATO. These secondary reactions solidify the sample and distribute heat back into the system. Simulations of cone calorimeter tests are performed in 1D and 2D axisymmetric models to explore the influence of anisotropic wood properties, particularly the orientation of wood fibers. Comparison of models with and without secondary reactions demonstrates their role in heat distribution and secondary char production and points out the experimentally observed difference in normalized mass between TGA and cone calorimeter tests. The model is verified by comparison with experimental results to show that it can simulate the complicated behavior of wood pyrolysis as well as emphasizes the importance of reaction pathways, secondary reactions, heat transfer, mass transfer and intra-pore interaction phenomena
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Arslanoglu, Yilmaz. "Genetic Algorithm For Personnel Assignment Problem With Multiple Objectives." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606880/index.pdf.
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This thesis introduces a multi-objective variation of the personnel assignment problem, by including additional hierarchical and team constraints, which put restrictions on possible matchings of the bipartite graph. Besides maximization of summation of weights that are assigned to the edges of the graph, these additional constraints are also treated as objectives which are subject to minimization. In this work, different genetic algorithm approaches to multi-objective optimization are considered to solve the problem. Weighted Sum &ndasha classical approach, VEGA - a non-elitist multi-objective evolutionary algorithm, and SPEA &ndash
a popular elitist multi-objective evolutionary algorithm, are considered as means of solution to the problem, and their performances are compared with respect to a number of multi-objective optimization criteria.
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Martz, Matthew. "Preliminary Design of an Autonomous Underwater Vehicle Using a Multiple-Objective Genetic Optimizer." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33291.
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The process developed herein uses a Multiple Objective Genetic Optimization (MOGO) algorithm. The optimization is implemented in ModelCenter (MC) from Phoenix Integration. It uses a genetic algorithm that searches the design space for optimal, feasible designs by considering three Measures of Performance (MOPs): Cost, Effectiveness, and Risk. The complete synthesis model is comprised of an input module, the three primary AUV synthesis modules, a constraint module, three objective modules, and a genetic algorithm. The effectiveness rating determined by the synthesis model is based on nine attributes identified in the US Navyâ s UUV Master Plan and four performance-based attributes calculated by the synthesis model. To solve multi-attribute decision problems the Analytical Hierarchy Process (AHP) is used. Once the MOGO has generated a final generation of optimal, feasible designs the decision-maker(s) can choose candidate designs for further analysis. A sample AUV Synthesis was performed and five candidate AUVs were analyzed.Master of Science
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Damay, Nicolas. "Multiple-objective optimization of traffic lightsusing a genetic algorithm and a microscopic traffic simulator." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168413.
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Given the demand for mobility in our society, the cost of building additionalinfrastructures and the increasing concerns about the sustainability of the trafficsystem, traffic managers have to come up with new tools to optimize the trafficconditions within the existing infrastructure. This study considered to optimizethe durations of the green light phases in order to improve several criteria such asthe ability of the network to deal with important demands or the total pollutantemissions. Because the modeling of the problem is difficult and computationally demanding,a stochastic micro-simulator called ’Simulation of Urban MObility’ (SUMO) has been used with a stochastic optimization process, namely a Genetic Algorithm (GA). The research objective of the study was to create a computational frameworkbased on the integration of SUMO and a Multi-Objective Genetic-Algorithm (MOGA).The proposed framework was demonstrated on a medium-size network correspondingto a part of the town of Rouen, France. This network is composed of 11 intersections,168 traffic lights and 40 possible turning movements. The network is monitored with20 sensors, spread over the network. The MOGA considered in this study is basedon NSGA-II. Several aspects have been investigated during the course of this thesis. An initial study shows that the proposed MOGA is successful in optimizing thesignal control strategies for a medium-sized network within a reasonable amount oftime. A second study has been conducted to optimize the demand-related model ofSUMO in order to ensure that the behavior in the simulated environment is close tothe real one. The study shows that a hybrid algorithm composed of a gradient searchalgorithm combined with a GA achieved a satisfactory behavior2 for a medium-sizenetwork within a reasonable time.
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Pennada, Venkata Sai Teja. "Solving Multiple Objective Optimization Problem using Multi-Agent Systems: A case in Logistics Management." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20745.
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Background: Multiple Objective Optimization problems(MOOPs) are common and evident in every field. Container port terminals are one of the fields in which MOOP occurs. In this research, we have taken a case in logistics management and modelled Multi-agent systems to solve the MOOP using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Objectives: The purpose of this study is to build AI-based models for solving a Multiple Objective Optimization Problem occurred in port terminals. At first, we develop a port agent with an objective function of maximizing throughput and a customer agent with an objective function of maximizing business profit. Then, we solve the problem using the single-objective optimization model and multi-objective optimization model. We then compare the results of both models to assess their performance. Methods: A literature review is conducted to choose the best algorithm among the existing algorithms, which were used previously in solving other Multiple Objective Optimization problems. An experiment is conducted to know how well the models performed to solve the problem so that all the participants are benefited simultaneously. Results: The results show that all three participants that are port, customer one and customer two have gained profits by solving the problem in multi-objective optimization model. Whereas in a single-objective optimization model, a single participant has achieved earnings at a time, leaving the rest of the participants either in loss or with minimal profits. Conclusion: We can conclude that multi-objective optimization model has performed better than the single-objective optimization model because of the impartial results among the participants.
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Perez, Gallardo Jorge Raúl. "Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10505/1/perez_gallardo_partie_1_sur_2.pdf.
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Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.
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Tamayo, Cascan Edgar. "Towards using microscopic traffic simulations for safety evaluation." Thesis, KTH, Fordonsdynamik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243486.
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Microscopic traffic simulation has become an important tool to investigate traffic efficiency and road safety. In order to produce meaningful results, incorporated driver behaviour models need to be carefully calibrated to represent real world conditions. In addition to macroscopic relationships such as the speed-density diagram, they should also adequately represent the average risk of accidents occurring on the road. In this thesis, I present a two stage computationally feasible multi-objective calibration process. The first stage performs a parameter sensitivity analysis to select only parameters with considerable effect on the respective objective functions to keep the computational complexity of the calibration at a manageable level. The second stage employs a multi-objective genetic algorithm that produces a front of Pareto optimal solutions with respect to the objective functions. Compared to traditional methods which focus on only one objective while sacrificing accuracy of the other, my method achieves a high degree of realism for both traffic flow and average risk.Mikroskopisk trafiksimulering har blivit ett viktigt verktyg för att undersöka trafik effektivitet och trafiksäkerhet. För att producera meningsfulla resultat måste inbyggda drivrutinsbeteendemodeller noggrant kalibreras för att representera verkliga förhållanden i världen. Förutom makroskopiska relationer, såsom hastighetsdensitetsdiagrammet, bör de också på ett adekvat sätt representera den genomsnittliga risken för olyckor som uppträder på vägen. I denna avhandling presenterar jag en tvåstegs beräkningsberättigbar mångsidig kalibreringsprocess. Det första steget utför en parameterkänslighetsanalysför att bara välja parametrar med stor effekt på respektive objektiv funktioner för att hålla kalibrerings komplexiteten på en hanterbar nivå. Det andra steget använder en mångriktig genetisk algoritm som ger framsidan av Pareto optimala lösningar med hänsyn till objektivfunktionerna. Jämfört med traditionella metoder som fokuserar på endast ett mål, samtidigt som man offrar den andra, ger min metod en hög grad av realism för både trafikflöde och genomsnittlig risk.
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Le, Trung-Dung. "Gestion de masses de données dans une fédération de nuages informatiques." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S101.
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Les fédérations de nuages informatiques peuvent être considérées comme une avancée majeure dans l’informatique en nuage, en particulier dans le domaine médical. En effet, le partage de données médicales améliorerait la qualité des soins. La fédération de ressources permettrait d'accéder à toutes les informations, même sur une personne mobile, avec des données hospitalières distribuées sur plusieurs sites. En outre, cela permettrait d’envisager de plus grands volumes de données sur plus de patients et ainsi de fournir des statistiques plus fines. Les données médicales sont généralement conformes à la norme DICOM (Digital Imaging and Communications in Medicine). Les fichiers DICOM peuvent être stockés sur différentes plates-formes, telles qu’Amazon, Microsoft, Google Cloud, etc. La gestion des fichiers, y compris le partage et le traitement, sur ces plates-formes, suit un modèle de paiement à l’utilisation, selon des modèles de prix distincts et en s’appuyant sur divers systèmes de gestion de données (systèmes de gestion de données relationnelles ou SGBD ou systèmes NoSQL). En outre, les données DICOM peuvent être structurées en lignes ou colonnes ou selon une approche hybride (ligne-colonne). En conséquence, la gestion des données médicales dans des fédérations de nuages soulève des problèmes d’optimisation multi-objectifs (MOOP - Multi-Objective Optimization Problems) pour (1) le traitement des requêtes et (2) le stockage des données, selon les préférences des utilisateurs, telles que le temps de réponse, le coût monétaire, la qualités, etc. Ces problèmes sont complexes à traiter en raison de la variabilité de l’environnement (liée à la virtualisation, aux communications à grande échelle, etc.). Pour résoudre ces problèmes, nous proposons MIDAS (MedIcal system on clouD federAtionS), un système médical sur les fédérations de groupes. Premièrement, MIDAS étend IReS, une plate-forme open source pour la gestion de flux de travaux d’analyse sur des environnements avec différents systèmes de gestion de bases de données. Deuxièmement, nous proposons un algorithme d’estimation des valeurs de coût dans une fédération de nuages, appelé Algorithme de régression %multiple linéaire dynamique (DREAM). Cette approche permet de s’adapter à la variabilité de l'environnement en modifiant la taille des données à des fins de formation et de test, et d'éviter d'utiliser des informations expirées sur les systèmes. Troisièmement, l’algorithme génétique de tri non dominé à base de grilles (NSGA-G) est proposé pour résoudre des problèmes d’optimisation multi-crtières en présence d’espaces de candidats de grande taille. NSGA-G vise à trouver une solution optimale approximative, tout en améliorant la qualité du font de Pareto. En plus du traitement des requêtes, nous proposons d'utiliser NSGA-G pour trouver une solution optimale approximative à la configuration de données DICOM. Nous fournissons des évaluations expérimentales pour valider DREAM, NSGA-G avec divers problèmes de test et jeux de données. DREAM est comparé à d'autres algorithmes d'apprentissage automatique en fournissant des coûts estimés précis. La qualité de la NSGA-G est comparée à celle des autres algorithmes NSGA présentant de nombreux problèmes dans le cadre du MOEA. Un jeu de données DICOM est également expérimenté avec NSGA-G pour trouver des solutions optimales. Les résultats expérimentaux montrent les qualités de nos solutions en termes d'estimation et d'optimisation de problèmes multi-objectifs dans une fédération de nuagesCloud federations can be seen as major progress in cloud computing, in particular in the medical domain. Indeed, sharing medical data would improve healthcare. Federating resources makes it possible to access any information even on a mobile person with distributed hospital data on several sites. Besides, it enables us to consider larger volumes of data on more patients and thus provide finer statistics. Medical data usually conform to the Digital Imaging and Communications in Medicine (DICOM) standard. DICOM files can be stored on different platforms, such as Amazon, Microsoft, Google Cloud, etc. The management of the files, including sharing and processing, on such platforms, follows the pay-as-you-go model, according to distinct pricing models and relying on various systems (Relational Data Management Systems or DBMSs or NoSQL systems). In addition, DICOM data can be structured following traditional (row or column) or hybrid (row-column) data storages. As a consequence, medical data management in cloud federations raises Multi-Objective Optimization Problems (MOOPs) for (1) query processing and (2) data storage, according to users preferences, related to various measures, such as response time, monetary cost, qualities, etc. These problems are complex to address because of heterogeneous database engines, the variability (due to virtualization, large-scale communications, etc.) and high computational complexity of a cloud federation. To solve these problems, we propose a MedIcal system on clouD federAtionS (MIDAS). First, MIDAS extends IReS, an open source platform for complex analytics workflows executed over multi-engine environments, to solve MOOP in the heterogeneous database engines. Second, we propose an algorithm for estimating of cost values in a cloud environment, called Dynamic REgression AlgorithM (DREAM). This approach adapts the variability of cloud environment by changing the size of data for training and testing process to avoid using the expire information of systems. Third, Non-dominated Sorting Genetic Algorithm based ob Grid partitioning (NSGA-G) is proposed to solve the problem of MOOP is that the candidate space is large. NSGA-G aims to find an approximate optimal solution, while improving the quality of the optimal Pareto set of MOOP. In addition to query processing, we propose to use NSGA-G to find an approximate optimal solution for DICOM data configuration. We provide experimental evaluations to validate DREAM, NSGA-G with various test problem and dataset. DREAM is compared with other machine learning algorithms in providing accurate estimated costs. The quality of NSGA-G is compared to other NSGAs with many problems in MOEA framework. The DICOM dataset is also experimented with NSGA-G to find optimal solutions. Experimental results show the good qualities of our solutions in estimating and optimizing Multi-Objective Problem in a cloud federation
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Honnanayakanahalli, Ramakrishna Prajwal. "MODELING, SIMULATION AND OPTIMIZATION OF A SUBMERGED RENEWABLE STORAGE SYSTEM INTEGRATED TO A FLOATING WIND FARM : A feasibility case study on the Swedish side of the Baltic sea, based on the geographical and wind conditions." Thesis, Mälardalens högskola, Framtidens energi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42321.
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Mathematical modeling and simulations of a submerged renewable storage system integrated to a wind farm, chosen based on the geographical and wind conditions at the Baltic Sea, gives insight on the feasibility of the submerged renewable storage and an approximation of the payback period and profits that could be generated. Genetic Algorithms were used to obtain the optimal number of spheres for a certain depth, based on 2 objective functions I.e. Minimum Life Cycle Cost (LCC) and maximum reduction in wind curtailment. The new arrangement concept shows that the Initial Capital Cost (ICC) could be decreased by 25% to 60% depending upon the number of sphere employed. Based on the inputs considered in the study, the results prove that the submerged renewable storage system would be feasible, and the profits ranging from 15 Million Euro to 29 Million Euro can be achieved at the chosen location, towards the Swedish side of the Baltic sea. Although, in a real life scenario it is assumed that only up to half of the profits obtained in the results would be achievable. The results also show that, the Pump/Turbine with a high turbine efficiency and lower pump efficiency, generated better profits, compared to a Pump/Turbine running with a higher pump efficiency and lower turbine efficiency. An attempt to increase the round-trip efficiency by adding a multi stage submersible pump, resulted in additional ICC and LCC, which saw a decrease in profits.
Book chapters on the topic "Multiple Objective Genetic Algorithm (MOGA)"
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Hamada, Naoki, Jun Sakuma, Shigenobu Kobayashi, and Isao Ono. "Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA." In Parallel Problem Solving from Nature – PPSN X, 691–701. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87700-4_69.
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Jaszkiewicz, Andrzej. "Multiple Objective Genetic Local Search Algorithm." In Lecture Notes in Economics and Mathematical Systems, 231–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56680-6_21.
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Roe, Michael, Wei Xu, and Dongping Song. "Evaluating the Multiple Objective Genetic Algorithm." In Optimizing Supply Chain Performance, 177–84. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137501158_10.
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Zolpakar, Nor Atiqah, Swati Singh Lodhi, Sunil Pathak, and Mohita Anand Sharma. "Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes." In Springer Series in Advanced Manufacturing, 185–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19638-7_8.
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Yadav, Deepti, Arunima Verma, and Frank Tittel. "Permanent Magnet Synchronous Motor (PMSM) Drive Using Multi-Objective Genetic Algorithm (MOGA) Technique." In Lecture Notes in Electrical Engineering, 587–97. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0312-0_58.
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Gopal, Mahesh. "Experimental Investigation of Duplex Stainless Steel Using RSM and Multi-objective Genetic Algorithm (MOGA)." In Lecture Notes in Mechanical Engineering, 813–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9809-8_59.
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Wang, Jiannan. "Utilization of Multi-Objective Genetic Algorithm (MOGA) to Optimize Maintenance Decision Support for Ordinary Arterial Highways." In Sustainable Civil Infrastructures, 80–89. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78276-3_8.
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Cruz Hernández, Heriberto, and Luis Gerardo de la Fraga. "Fitting Multiple Ellipses with PEARL and a Multi-objective Genetic Algorithm." In Numerical and Evolutionary Optimization – NEO 2017, 89–107. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96104-0_4.
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Fu, Yaping, Hongfeng Wang, and Min Huang. "Locate Multiple Pareto Optima Using a Species-Based Multi-objective Genetic Algorithm." In Communications in Computer and Information Science, 128–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45049-9_21.
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Dehuri, Satchidananda, Susmita Ghosh, and Ashish Ghosh. "Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases." In Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77467-9_1.
Full textConference papers on the topic "Multiple Objective Genetic Algorithm (MOGA)"
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Seisie-Amoasi, Ebenezer, Brian G. Williams, and Marco P. Schoen. "Optimization of a Star Pattern Recognition Algorithm for Attitude Determination Using a Multi-Objective Genetic Algorithm." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79774.
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Attitude determination for unmanned spacecrafts usually employs star trackers. The specifications for these devices dictate fast, reliable, robust, and autonomous algorithms to satisfy various mission constraints. This results into simple algorithms for reduced power consumption and reduced overall weight. Optimizing a Star Pattern Recognition Algorithm (SPRA), using an imbedded star map, requires the optimization of the genetic operators that constitute the SPRA and the control parameters within the SPRA. Simultaneous optimization of the control parameters of the SPRA results into a multi-objective and multi-parameter constrained optimization problem. The optimizing of genetic algorithms is often time consuming and rather tedious by nature. In this work, a Multi-Objective Genetic Algorithm (MOGA) acting as a meta-level GA is applied together with a double objective transition selection scheme to achieve the optimization. This approach results in significantly expediting the cost assignment process. By evolving a pareto set, an optimization population element rule is determined to exist between the control parameters of the SPRA. The existence of this rule ensures effective balance between population exploitation and exploration in the algorithm estimation process. This leads to effective solutions for finding the optimum with multiple concurrent objectives while taking the constraints into consideration. Simulation results using the optimized parameters for the SPRA indicate an improvement of the recognition accuracy from less than 60% to 100% as well as a reduction of the processing time of over 2000 generations to under 250 generations at 99% precision.
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Xuanyuan, Sisi, Zhaoliang Jiang, Lalit Patil, Yan Li, and Zhaoqian Li. "Multi-Objective Optimization of Product Configuration." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49871.
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In the context of globalization and mass customization, selecting the appropriate product configuration requires a simultaneous consideration of multiple criteria or objectives, which are in conflict with each other. The large solution space implies that analyzing each feasible solution is a combinatorial problem. Furthermore, no single optimal solution exists; on the contrary, there is a set of valid optimal solutions, i.e., the solution set is Pareto-optimal. We present the configuration problem from the perspective of using two types of attributes: static, i.e., the attributes that have pre-defined and constant values throughout the configuration process, and dynamic, i.e., attributes whose values vary according to decisions that are being made during the configuration process. We pose the product configuration as a multiobjective optimization problem requiring that multiple objective functions cannot be combined into a single objective function. We demonstrate the applicability of using Multi-Objective Genetic Algorithms (MOGA) to solve the problem and converge to a Pareto-optimal solution set from the large number of feasible solutions.
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Ju, Yaping, and Chuhua Zhang. "Multi-Objective Optimization Design Method for Tandem Compressor Cascade at Design and Off Design Conditions." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22655.
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Recently, there has been a renewed interest in the research of tandem compressor cascades due to the high stage pressure ratio and low control cost. Firstly, the computational fluid dynamics (CFD) method is employed to examine the particular aerodynamic performance of the tandem cascade. Then we propose an automatic multi-objective optimization design method of the tandem cascade for the superior aerodynamic performance under the multiple operation conditions. Particular efforts have been devoted to the gap geometry optimization in terms of the front and aft airfoil relative position, camber turning ratio as well as chord ratio. The multi-objective optimization algorithm comprises a refined multi-objective genetic algorithm (MOGA) and a developed artificial neural network (ANN) model which is used to fast approximate the aerodynamic performance of the tandem cascade. The results show that the tandem cascade outperforms the single cascade in terms of producing higher pressure ratio and lower losses while the operation range is rather narrow. The optimized all-better-than (ABT) tandem cascade has its design point performance significantly improved while the operation range slightly widened. We also find that a slight axial proximity and separation of the tandem airfoils are beneficial to widening the positive and negative operation range, respectively. This research is useful to the tandem compressor cascade design in minimizing the stage number of the engine compressors.
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Al-Turki, Ali, Obai Alnajjar, Majdi Baddourah, and Babatunde Moriwawon. "Compressed Dimension of Reservoir Models Uncertainty Parameters for Optimized Model Calibration and History Matching Process." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206066-ms.
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Abstract The algorithms and workflows have been developed to couple efficient model parameterization with stochastic, global optimization using a Multi-Objective Genetic Algorithm (MOGA) for global history matching, and coupled with an advanced workflow for streamline sensitivity-based inversion for fine-tuning. During parameterization the low-rank subsets of most influencing reservoir parameters are identified and propagated to MOGA to perform the field-level history match. Data misfits between the field historical data and simulation data are calculated with multiple realizations of reservoir models that quantify and capture reservoir uncertainty. Each generation of the optimization algorithms reduces the data misfit relative to the previous iteration. This iterative process continues until a satisfactory field-level history match is reached or there are no further improvements. The fine-tuning process of well-connectivity calibration is then performed with a streamlined sensitivity-based inversion algorithm to locally update the model to reduce well-level mismatch. In this study, an application of the proposed algorithms and workflow is demonstrated for model calibration and history matching. The synthetic reservoir model used in this study is discretized into millions of grid cells with hundreds of producer and injector wells. It is designed to generate several decades of production and injection history to evaluate and demonstrate the workflow. In field-level history matching, reservoir rock properties (e.g., permeability, fault transmissibility, etc.) are parameterized to conduct the global match of pressure and production rates. Grid Connectivity Transform (GCT) was used and assessed to parameterize the reservoir properties. In addition, the convergence rate and history match quality of MOGA was assessed during the field (global) history matching. Also, the effectiveness of the streamline-based inversion was evaluated by quantifying the additional improvement in history matching quality per well. The developed parametrization and optimization algorithms and workflows revealed the unique features of each of the algorithms for model calibration and history matching. This integrated workflow has successfully defined and carried uncertainty throughout the history matching process. Following the successful field-level history match, the well-level history matching was conducted using streamline sensitivity-based inversion, which further improved the history match quality and conditioned the model to historical production and injection data. In general, the workflow results in enhanced history match quality in a shorter turnaround time. The geological realism of the model is retained for robust prediction and development planning.
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Feng, Xiaolong, Daniel Wa¨ppling, Hans Andersson, Johan O¨lvander, and Mehdi Tarkian. "Multi-Objective Optimization in Industrial Robotic Cell Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28488.
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It has become a common practice to conduct simulation-based design of industrial robotic cells, where Mechatronic system model of an industrial robot is used to accurately predict robot performance characteristics like cycle time, critical component lifetime, and energy efficiency. However, current robot programming systems do not usually provide functionality for finding the optimal design of robotic cells. Robot cell designers therefore still face significant challenge to manually search in design space for achieving optimal robot cell design in consideration of productivity measured by the cycle time, lifetime, and energy efficiency. In addition, robot cell designers experience even more challenge to consider the trade-offs between cycle time and lifetime as well as cycle time and energy efficiency. In this work, utilization of multi-objective optimization to optimal design of the work cell of an industrial robot is investigated. Solution space and Pareto front are obtained and used to demonstrate the trade-offs between cycle-time and critical component lifetime as well as cycle-time and energy efficiency of an industrial robot. Two types of multi-objective optimization have been investigated and benchmarked using optimal design problem of robotic work cells: 1) single-objective optimization constructed using Weighted Compromise Programming (WCP) of multiple objectives and 2) Pareto front optimization using multi-objective generic algorithm (MOGA-II). Of the industrial robotics significance, a combined design optimization problem is investigated, where design space consisting of design variables defining robot task placement and robot drive-train are simultaneously searched. Optimization efficiency and interesting trade-offs have been explored and successful results demonstrated.
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Zhang, Jian, Heejin Cho, and Pedro Mago. "Optimal Design of Integrated Distributed Energy Systems for Off-Grid Buildings in Different U.S. Regions." In ASME 2021 15th International Conference on Energy Sustainability collocated with the ASME 2021 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/es2021-60503.
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Abstract Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies, however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various U.S. climate regions. Distributed energy systems consisting of renewable and non-renewable power generation technologies with energy storage are employed to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power systems (CHP), solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building’s electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building’s thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of DERs and each component’s size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a tradeoff between economic and environmental performance.
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Briones, Alejandro M., Nathan Thomas, and Brent A. Rankin. "Effects of Combustor Enclosure Flow Path on Combustor Design." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14127.
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Abstract A design optimization procedure was implemented to resize the holes of a combustor liner for practical applications. A combustor geometry evaluated without an enclosure was to be reformulated within an enclosure. The objective functions of the combustor with enclosure involved targeting the flow splits of the combustor without enclosure. Latin Hypercube Sampling (LHS) design of experiments (DOE) was utilized to obtain at least a pure quadratic response surface (RS). These were computed using Genetic Aggregate (GA). These RS were, in turn, evaluated by a multiple objective genetic algorithm (MOGA) optimizer. The focus of this study was a small-scale cavity-stabilized combustor. Steady, compressible three-dimensional simulations are performed using a multi-phase Realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach. Combustion-turbulence interaction is modeled with flamelet progress variable (FPV) and β-presumed probability density function (PDF). There are eleven input and output parameters corresponding to the combustor hole sizes and associated mass flow rates. The RS obtained with GA were principally of the Kriging kind (with constant and linear trends and damped sinusoid and Gaussian kernels). A combustor hole mass flow rate was mainly determined by its hole size but was also influenced by the other holes. The combustor flow split non-linearity shows that increasing a hole size increases its mass flow rate, but simultaneously decreases another hole flow rate. This was also verified by sensitivity analysis. Due to this non-linearity, matching flow splits between geometry without and with enclosure is challenging and may not be possible for some situations. Thus, it is concluded that optimization of the combustor geometry without the enclosure is not the best route. Rather, it would be better for the geometry to be optimized with the enclosure included in order to account for flow separation and non-linear influence of the combustor holes on the flow field.
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Li, Mian. "An Improved Kriging Assisted Multi-Objective Genetic Algorithm." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28543.
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Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. We present an improved kriging assisted MOGA, called Circled Kriging MOGA (CK-MOGA), in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Three numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and our developed Kriging MOGA in terms of the number of simulation calls.
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Weström, Jakob, Xiaolong Feng, Hans Andersson, and Stefan Lunderius. "Optimal Spring Balancing Cylinder Design of an Industrial Robot Using Multi-Disciplinary and Multi-Objective Optimization." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82252.
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This article presents an automated approach in optimal design of the spring balancing cylinder of an industrial robot using multi-disciplinary and multi-objective design optimization. Spring balancing cylinder is a mechanical device typically used in industrial robots of high load handling capacity. The objective of use of such device is to effectively balance one of main axes (typically axis-2) subject to the most severe gravitational torque. The spring balancing cylinder consists typically of multiple springs (two or three) co-axially installed inside the cylinder. Design of such balancing device involves about 16 design parameters, including both geometric parameters (free length, wire diameter, spring outer diameter, and number of turns) and parameters defining mounting positions of the device on a robot. Optimal design of such device is to achieve desired balancing, measured by maximum unbalanced static torque of the balanced axis, with minimum weight and volume of the cylinder. More desirably, the trade-off relationship between the maximum static torque measured by a balancing degree index and weight of the balancing cylinder is explored. Design of such balancing device is subject to a number of hard constraints defining fatigue lifetime of the springs and geometric interference between adjacent springs both in radial and axial directions. Solving of this design problem requires use of two different design tools. The first design tool is a robot static design tool. The entire robot statics is modeled. The maximum static torque of the balanced axis is calculated by finding the maximum value of static torques of the axis as function rotational angles of the axis within its limits. The maximum static torque is used as one of the design objectives. The second design tool is a detailed spring dimensioning tool. The overall spring constant and pre-loading force are determined subject to constraints of geometric and fatigue lifetime. The integration of the design tools is accomplished using a commercial software tool modeFrontier. This challenging design problem is formulated into an optimization problem of mixed design variables, multi-objective and multi-constraint nature and solved fully automatically using Multi-Objective Genetic Algorithm (MOGA) implemented in the modeFrontier modeling and optimization environment. The trade-off relationship between the balancing degree and the weight of the springs have been quantitatively explored. Even though some limitations of the developed methodology do exist and need further improvement, it is convinced that the developed approach is ready to be applied in industrial design practice.
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Li, Mian, Genzi Li, and Shapour Azarm. "A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99316.
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The high computational cost of population based optimization methods, such as multi-objective genetic algorithms, has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of computationally intensive simulation (objective/constraint functions) calls. We present a new multi-objective design optimization approach in that kriging-based metamodeling is embedded within a multi-objective genetic algorithm. The approach is called Kriging assisted Multi-Objective Genetic Algorithm, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points or individuals are evaluated by kriging metamodels, which are computationally inexpensive, instead of the simulation. The decision as to whether the simulation or their kriging metamodels to be used for evaluating an individual is based on checking a simple condition. That is, it is determined whether by using the kriging metamodels for an individual the non-dominated set in the current generation is changed. If this set is changed, then the simulation is used for evaluating the individual; otherwise, the corresponding kriging metamodels are used. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average, K-MOGA converges to the Pareto frontier with about 50% fewer number of simulation calls compared to a conventional MOGA.
Reports on the topic "Multiple Objective Genetic Algorithm (MOGA)"
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Allen, Luke, Joon Lim, Robert Haehnel, and Ian Dettwiller. Helicopter rotor blade multiple-section optimization with performance. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41031.
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This paper presents advancements in a surrogate-based, rotor blade design optimization framework for improved helicopter performance. The framework builds on previous successes by allowing multiple airfoil sections to designed simultaneously to minimize required rotor power in multiple flight conditions. Rotor power in hover and forward flight, at advance ratio 𝜇 = 0.3, are used as objective functions in a multi-objective genetic algorithm. The framework is constructed using Galaxy Simulation Builder with optimization provided through integration with Dakota. Three independent airfoil sections are morphed using ParFoil and aerodynamic coefficients for the updated airfoil shapes (i.e., lift, drag, moment) are calculated using linear interpolation from a database generated using C81Gen/ARC2D. Final rotor performance is then calculated using RCAS. Several demonstrative optimization case studies were conducted using the UH-60A main rotor. The degrees of freedom for this case are limited to the airfoil camber, camber crest position, thickness, and thickness crest position for each of the sections. The results of the three-segment case study show improvements in rotor power of 4.3% and 0.8% in forward flight and hover, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 6.0% reduction at 𝜇 = 0.35, and 8.8% reduction at 𝜇 = 0.4.