Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all ex...

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Vydané v:IEEE transactions on evolutionary computation Ročník 26; číslo 2; s. 379 - 391
Hlavní autori: Yuan, Jiawei, Liu, Hai-Lin, Ong, Yew-Soon, He, Zhaoshui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Constraint handling
Constraints
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Fragments
Genetic algorithms
indicator
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Population
Search problems
Sociology
Statistics
Uninterruptible power systems
ISSN:1089-778X, 1941-0026
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Shrnutí:To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3089155