Enhancing Diversity by Local Subset Selection in Evolutionary Multiobjective Optimization

The main target of multiobjective evolutionary algorithms (MOEAs) is to find a set of evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multiobjective optimization problem (MOP). This means that the approximated set should be as close to the PF as possible, and as...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2023-10, Vol.27 (5), p.1456-1469
Main Authors: Wang, Zihan, Mao, Bochao, Hao, Hao, Hong, Wenjing, Xiao, Chunyun, Zhou, Aimin
Format: Article
Language:English
Subjects:
Approximation
Approximation algorithms
Criteria
Evolutionary algorithm
Evolutionary algorithms
local search
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Potential energy
potential energy function
Search methods
Search problems
Sociology
Sorting
Statistics
subset selection
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Summary:The main target of multiobjective evolutionary algorithms (MOEAs) is to find a set of evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multiobjective optimization problem (MOP). This means that the approximated set should be as close to the PF as possible, and as diverse as possible. The former is usually called a convergence criterion and the latter is called a diversity criterion. A variety of strategies have been proposed to meet the two criteria. However, as far as the diversity criterion is concerned, it is still a challenge to achieve an evenly distributed approximation set with different sizes for a problem with a complicated PF shape. To deal with this challenge, we propose a local subset selection (LSS) -based environmental selection for evolutionary multiobjective optimization in this article. LSS considers the environmental selection as a subset selection problem by choosing promising solutions from the combination of the parent and offspring populations. In LSS, a potential energy function is utilized as the objective function, which provides a heavy selection pressure on diversity as well as has low computational complexity. Furthermore, to balance search efficiency and quality, a local search strategy is used in LSS to make full use of objective information for acceleration. The proposed LSS strategy is embedded into some state-of-the-art Pareto-domination-based MOEAs, and the experimental results suggest that LSS can produce shape-invariant and evenly distributed nondominated sets with different population sizes.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3194211