A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization

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 c...

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Vydané v:Entropy (Basel, Switzerland) Ročník 25; číslo 1; s. 13
Hlavní autori: Geng, Huantong, Zhou, Zhengli, Shen, Junye, Song, Feifei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 21.12.2022
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coevolution
Collaboration
constrained many-objective optimization
Constraints
Design
dual-population
evolutionary algorithm
Evolutionary algorithms
Feasibility
Genetic algorithms
Methods
Multiple objective analysis
Optimization
Populations
ε-constraint handling
constrained many-objective optimization
ε-constraint handling
coevolution
dual-population
evolutionary algorithm
ISSN:1099-4300, 1099-4300
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1099-4300
1099-4300
DOI:10.3390/e25010013