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 ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (ε-MOEA/D-DE). First,...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9330545/ |
| _version_ | 1818921918622859264 |
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| author | Bing-Jie Liu Xiao-Jun Bi |
| author_facet | Bing-Jie Liu Xiao-Jun Bi |
| author_sort | Bing-Jie Liu |
| collection | DOAJ |
| description | To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (ε-MOEA/D-DE). First, an adaptive ε-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. |
| first_indexed | 2024-12-20T01:45:17Z |
| format | Article |
| id | doaj.art-bfd7ed1b299c468fa43e02b8909b6346 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:45:17Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bfd7ed1b299c468fa43e02b8909b63462022-12-21T19:57:48ZengIEEEIEEE Access2169-35362021-01-019175961760910.1109/ACCESS.2021.30530419330545Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential EvolutionBing-Jie Liu0https://orcid.org/0000-0002-6249-382XXiao-Jun Bi1https://orcid.org/0000-0002-5382-1000College of Information and Communications Engineering, Harbin Engineering University, Harbin, ChinaDepartment of Information Engineering, Minzu University of China, Beijing, ChinaTo improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive ε-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (ε-MOEA/D-DE). First, an adaptive ε-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.https://ieeexplore.ieee.org/document/9330545/Constrained many-objective optimizationε-constrain handling techniquesdifferential evolution algorithmMOEA/D |
| spellingShingle | Bing-Jie Liu Xiao-Jun Bi Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution IEEE Access Constrained many-objective optimization ε-constrain handling techniques differential evolution algorithm MOEA/D |
| title | Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution |
| title_full | Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution |
| title_fullStr | Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution |
| title_full_unstemmed | Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution |
| title_short | Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution |
| title_sort | adaptive x03b5 constraint multi objective evolutionary algorithm based on decomposition and differential evolution |
| topic | Constrained many-objective optimization ε-constrain handling techniques differential evolution algorithm MOEA/D |
| url | https://ieeexplore.ieee.org/document/9330545/ |
| work_keys_str_mv | AT bingjieliu adaptivex03b5constraintmultiobjectiveevolutionaryalgorithmbasedondecompositionanddifferentialevolution AT xiaojunbi adaptivex03b5constraintmultiobjectiveevolutionaryalgorithmbasedondecompositionanddifferentialevolution |