Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula>-constraint multi-objective...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 17596 - 17609
Main Authors: Liu, Bing-Jie, Bi, Xiao-Jun
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Constrained many-objective optimization
Constraints
Convergence
Decomposition
differential evolution algorithm
Diversity reception
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Linear programming
MOEA/D
Multiple objective analysis
Optimization
Search problems
Sociology
Statistics
ε-constrain handling techniques
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula>-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (<inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula>-MOEA/D-DE). First, an adaptive <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula>-constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multi-objective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3053041