A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems
Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best part...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6238206 |
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| author | Fei Chen Shuhuan Wu Fang Liu Junkai Ji Qiuzhen Lin |
| author_facet | Fei Chen Shuhuan Wu Fang Liu Junkai Ji Qiuzhen Lin |
| author_sort | Fei Chen |
| collection | DOAJ |
| description | Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used. |
| format | Article |
| id | doaj-art-11b1652da86747d9bf8ccfbe941f94c8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-11b1652da86747d9bf8ccfbe941f94c82025-02-03T06:45:59ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/62382066238206A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization ProblemsFei Chen0Shuhuan Wu1Fang Liu2Junkai Ji3Qiuzhen Lin4College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaMost multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.http://dx.doi.org/10.1155/2020/6238206 |
| spellingShingle | Fei Chen Shuhuan Wu Fang Liu Junkai Ji Qiuzhen Lin A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems Complexity |
| title | A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems |
| title_full | A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems |
| title_fullStr | A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems |
| title_full_unstemmed | A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems |
| title_short | A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems |
| title_sort | novel angular guided particle swarm optimizer for many objective optimization problems |
| url | http://dx.doi.org/10.1155/2020/6238206 |
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