Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX)...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/2491796 |
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| author | Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao |
| author_facet | Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao |
| author_sort | Yifei Sun |
| collection | DOAJ |
| description | The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems. |
| format | Article |
| id | doaj-art-f14ad4450c044c63a132e65bd86bfae7 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-f14ad4450c044c63a132e65bd86bfae72025-02-03T05:57:20ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/2491796Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective OptimizationYifei Sun0Kun Bian1Zhuo Liu2Xin Sun3Ruoxia Yao4School of Physics and Information TechnologySchool of Physics and Information TechnologySchool of Physics and Information TechnologySchool of Physics and Information TechnologySchool of Computer ScienceThe decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems.http://dx.doi.org/10.1155/2021/2491796 |
| spellingShingle | Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization Discrete Dynamics in Nature and Society |
| title | Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization |
| title_full | Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization |
| title_fullStr | Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization |
| title_full_unstemmed | Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization |
| title_short | Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization |
| title_sort | adaptive strategies based on differential evolutionary algorithm for many objective optimization |
| url | http://dx.doi.org/10.1155/2021/2491796 |
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