Výsledky vyhledávání - reference vector-based evolutionary algorithms

An Adaptive Reference Vector-Based Interval Multiobjective Evolutionary Algorithm Autor Gan, Xingjia, Sun, Jing, Gong, Dunwei, Jia, Dongbao, Dai, Hongwei, Zhong, Zhaoman

“… Therefore, an adaptive reference vector-based interval multiobjective evolutionary algorithm in the framework of MOEA/D (IMOEA…”
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A strengthened diversity indicator and reference vector-based evolutionary algorithm for many-objective optimization Autor Sun, Wenjing, Li, Junhua

“… To address these issues, this paper proposes a strengthened diversity indicator and reference vector-based evolutionary algorithm for many-objective optimization, termed SDIEA…”
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A reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation Autor Jiao, Keming, Chen, Jie, Xin, Bin, Li, Li

“… As crossover operators can affect the offspring distribution, an adaptive operator selection and reference vector based evolutionary algorithm (OVEA…”
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An Adaptative Reference Vector Based Evolutionary Algorithm for Many-Objective Optimization Autor Li, Junhua, Chen, Guoyu, Li, Ming, Chen, Hao

“… This paper proposes an adaptive reference vector based evolutionary algorithm for many-objective optimization, termed as ARVEA…”
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A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms Autor Tanabe, Ryoji, Ishibuchi, Hisao

“… While decomposition-based evolutionary algorithms have good performance for multiobjective optimization, they are likely to perform poorly for multimodal multiobjective optimization due to the lack…”
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An enhanced reference vectors-based multi-objective evolutionary algorithm with neighborhood-based adaptive adjustment Autor Fan, Rui, Wei, Lixin, Sun, Hao, Hu, Ziyu

“… Inspired by this issue, an enhanced reference vectors-based multi-objective evolutionary algorithm with neighborhood-based adaptive adjustment (MOEA-NAA) is proposed…”
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A Reference-Vector-Based Strength Pareto Evolutionary Algorithm 2 Autor Zhang, Lu, Meng, Qinchao

“…In this paper, a reference-vector-based strength Pareto evolutionary algorithm 2 (RVSPEA2…”
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Two reference vector sets based evolutionary algorithm for many‐objective optimization Autor Qin, Cifeng, Ming, Fei, Gong, Wenyin

“… In this paper, a novel two reference vector sets based evolutionary algorithm, referred to as TVEA, is proposed to solve both types of MaOPs…”
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A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation Autor Zhao, Mingde, Ge, Hongwei, Zhang, Kai, Hou, Yaqing

“… This paper proposes a reference vector based algorithm which uses two interacting engines to adapt the reference vectors and to evolve the population towards the true Pareto Front (PF) s.t…”
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Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems Autor Zhou, Chong, Dai, Guangming, Zhang, Cuijun, Li, Xiangping, Ma, Ke

“… Reference points or weight vectors based evolutionary algorithms have been developed successfully for solving MaOPs…”
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Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework Autor Deb, Kalyanmoy, Lopes, Claudio Lucio do Val, Martins, Flávio Vinícius Cruzeiro, Wanner, Elizabeth Fialho

“…Evolutionary multiobjective and many-objective optimization (EMO and EMaO) algorithms are increasingly used to identify the true shape and location…”
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Identifying Pareto Fronts Reliably Using a Multi-Stage Reference-vector-based Framework Autor Deb, Kalyanmoy, Lopes, Claudio Lucio do Val, Martins, Flavio Vinicius Cruzeiro, Wanner, Elizabeth Fialho

“…Evolutionary multi-objective and many-objective optimization (EMO and EMaO) algorithms are increasingly being used to identify the true shape and location…”
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Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting Autor Liu, Chao, Gao, Fengfeng, Zhang, Mengwan, Li, Yuanrui, Qian, Cun

“…) of evolutionary attention mechanism and reference vector based clustering algorithm (RVMOC) and proposes a new prediction method of the stock market return rate…”
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Evolutionary algorithms in robot calibration Autor Bastl, Pavel, Chakraborti, Nirupam, Valášek, Michael

“… EvoDN2 was used together with a reference vector-based evolutionary…”
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A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies Autor Yang, Lei, Zhang, Yuanye, Cao, Jiale, Li, Kangshun, Wang, Dongya

“… Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs…”
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A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimization Autor Li, Wei, Chen, Yangtao, Dong, Yuehua, Huang, Ying

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A constrained many-objective evolutionary algorithm with learning vector quantization-based reference point adaptation Autor Wang, Chao, Huang, Huitao, Pan, Huimin

“… the distribution of feasible regions. To address this issue, we propose a constrained many-objective evolutionary algorithm with reference point adaptation…”
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An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors Autor Asafuddoula, Md, Singh, Hemant Kumar, Ray, Tapabrata

“…). Development of algorithms to solve MaOPs has garnered significant research attention in recent years…”
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A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization Autor Dhiman, Gaurav, Soni, Mukesh, Pandey, Hari Mohan, Slowik, Adam, Kaur, Harsimran

“…A novel hybrid many-objective evolutionary algorithm called Reference Vector Guided Evolutionary Algorithm based on hypervolume indicator (H-RVEA…”
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Efficient constrained large-scale multi-objective optimization based on reference vector-guided evolutionary algorithm Autor Fan, Chaodong, Wang, Jiawei, Yang, Laurence T., Xiao, Leyi, Ai, Zhaoyang

“… For the large-scale of decision variables, some reference vector-guided, competitive group optimization-based and pairwise child generation-based algorithms have improved the search efficiency of constrained LSMOPs…”
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