Search Results - Multiobjective Optimization in Evolutionary Algorithms

An enhanced sparse multiobjective evolutionary algorithm in large-scale multiobjective optimization by Huang, Xiaodong, Wang, Jian, Zhang, Kai, Yuan, Bin, Dai, Caili, Ablameyko, Sergey V.

“…In recent years, sparse large-scale multiobjective optimization problems (LSMOPs) have found widespread application in real-world scenarios and have become a focus of evolutionary computing research…”
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The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition by Felipe Campelo, Lucas S. Batista, Claus Aranha

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Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning by Luan, Yuxuan, He, Junjiang, Yang, Jingmin, Lan, Xiaolong, Yang, Geying

“… This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation…”
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A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems by Tang, Lixin, Wang, Xianpeng

“…Recently, the hybridization between evolutionary algorithms and other metaheuristics has shown very good performances in many kinds of multiobjective optimization problems (MOPs…”
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Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods by Liang, J. J., Xiao, J. M., Zhou, Q., Qu, B. Y., Suganthan, P. N.

“…Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously…”
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A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization by Liu, Songbai, Lin, Qiuzhen, Li, Jianqiang, Tan, Kay Chen

“…Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs…”
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Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization by Li, Ke, Chen, Renzhi, Fu, Guangtao, Yao, Xin

“… To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization…”
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Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm by Wang, Weizhong, Liu, Hai-Lin

“…In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping…”
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Robust Multiobjective Optimization via Evolutionary Algorithms by He, Zhenan, Yen, Gary G., Yi, Zhang

“… Nowadays, evolutionary algorithms (EAs) have been successfully applied to various multiobjective optimization problems (MOPs…”
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A Dual-Population-Based Evolutionary Algorithm for Constrained Multiobjective Optimization by Ming, Mengjun, Trivedi, Anupam, Wang, Rui, Srinivasan, Dipti, Zhang, Tao

“… Their imbalance can easily cause the failure of a constrained multiobjective evolutionary algorithm (CMOEA…”
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A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems by Aghaei pour, Pouya, Hakanen, Jussi, Miettinen, Kaisa

“…We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate…”
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Integrating Actor-Critic Reinforcement Learning With Evolutionary Algorithm for Multimodal Multiobjective Optimization by Xiang, Kaili, Huang, Tenglong, Yang, Lei, Lin, Jie

“…Effectively solving multimodal multiobjective optimization problems (MMOPs) requires maintaining an optimal balance between the diversity and the convergence…”
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MIJ2K Optimization using evolutionary multiobjective optimization algorithms by Bustamante, Alvaro Luis, Molina López, José M., Patricio, Miguel A.

“… The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems…”
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An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts by Jiang, Shouyong, Yang, Shengxiang

“…The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs…”
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An efficient solution strategy for bilevel multiobjective optimization problems using multiobjective evolutionary algorithm by Li, Hong, Zhang, Li

“…An efficient solution strategy is proposed for bilevel multiobjective optimization problem (BLMOP…”
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A Co-evolutionary Multi-population Evolutionary Algorithm for Dynamic Multiobjective Optimization by Xu, Xin-Xin, Li, Jian-Yu, Liu, Xiao-Fang, Gong, Hui-Li, Ding, Xiang-Qian, Jeon, Sang-Woon, Zhan, Zhi-Hui

“…Dynamic multiobjective optimization problems (DMOPs) widely appear in various real-world applications and have attracted increasing attention worldwide…”
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A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization by Jiang, Shouyong, Yang, Shengxiang

“… preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization…”
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Weighted Indicator-Based Evolutionary Algorithm for Multimodal Multiobjective Optimization by Li, Wenhua, Zhang, Tao, Wang, Rui, Ishibuchi, Hisao

“…) sets correspond to the same point on the Pareto front. Traditional multiobjective evolutionary algorithms (MOEAs…”
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Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker by Battiti, Roberto, Passerini, Andrea

“… This paper adopts the methodology of reactive search optimization (RSO) for evolutionary interactive multiobjective optimization…”
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A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multiobjective Optimization by Ming, Mengjun, Wang, Rui, Ishibuchi, Hisao, Zhang, Tao

“…). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions and, therefore, exhibit poor performance when facing problems with large infeasible regions…”
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