MOEA3H: Multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical environment

Generating feasible solution and selecting valuable solution are the most important issues when dealing with complicated multi-objective problems. Focusing on these issues, the mechanism of multi-objective problem is analyzed by evolutionary history and environmental information. Hierarchical decisi...

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Vydané v:ISA transactions Ročník 129; číslo Pt A; s. 56 - 68
Hlavní autori: Hu, Ziyu, Li, Zihan, Sun, Hao, Wei, Lixin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.10.2022
Predmet:
Dynamic multi-objective optimization
Evolutionary algorithm
Objective decomposition
Pareto front
Pareto front
Objective decomposition
Dynamic multi-objective optimization
Evolutionary algorithm
ISSN:0019-0578, 1879-2022, 1879-2022
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Shrnutí:Generating feasible solution and selecting valuable solution are the most important issues when dealing with complicated multi-objective problems. Focusing on these issues, the mechanism of multi-objective problem is analyzed by evolutionary history and environmental information. Hierarchical decision based on rank fitness of distance correlation is proposed to guide the evolutionary operator. Heuristic learning by dynamic evolutionary is introduced to deal with static optimization problem. History information acquired from solution landscape is used to achieve a comprehensive search on feasible region. Based on these improvement, multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical environment (MOEA3H) is proposed. The proposed algorithm performs best on 10 and 14 of 19 test problems on IGD and Hvpervolume, respectively. •Hierarchical decision based on rank fitness of distance correlation is proposed to guide the evolutionary operator.•Heuristic learning by dynamic evolutionary is introduced to deal with static optimization problem.•History information acquired from solution landscape is used to achieve a comprehensive search on feasible region.•The sub-region density estimation algorithm is introduced to make full use of the information in the evolution process.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2021.12.038