A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules
In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/522 |
| _version_ | 1797623941830803456 |
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| author | Eryang Guo Yuelin Gao Chenyang Hu Jiaojiao Zhang |
| author_facet | Eryang Guo Yuelin Gao Chenyang Hu Jiaojiao Zhang |
| author_sort | Eryang Guo |
| collection | DOAJ |
| description | In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness. |
| first_indexed | 2024-03-11T09:35:55Z |
| format | Article |
| id | doaj.art-ca29756314d44f96a6342f1b0e3e755d |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:35:55Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ca29756314d44f96a6342f1b0e3e755d2023-11-16T17:20:50ZengMDPI AGMathematics2227-73902023-01-0111352210.3390/math11030522A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility RulesEryang Guo0Yuelin Gao1Chenyang Hu2Jiaojiao Zhang3School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, ChinaSchool of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, ChinaSchool of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, ChinaSchool of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, ChinaIn this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.https://www.mdpi.com/2227-7390/11/3/522constraint optimizationsparticle swarm optimizationdifferential evolutionfeasibility rulesengineering optimization problems |
| spellingShingle | Eryang Guo Yuelin Gao Chenyang Hu Jiaojiao Zhang A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules Mathematics constraint optimizations particle swarm optimization differential evolution feasibility rules engineering optimization problems |
| title | A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules |
| title_full | A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules |
| title_fullStr | A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules |
| title_full_unstemmed | A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules |
| title_short | A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules |
| title_sort | hybrid pso de intelligent algorithm for solving constrained optimization problems based on feasibility rules |
| topic | constraint optimizations particle swarm optimization differential evolution feasibility rules engineering optimization problems |
| url | https://www.mdpi.com/2227-7390/11/3/522 |
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