A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization
Most existing particle swarm optimization (PSO) variants use a single learning strategy and a fixed neighborhood structure for all particles during the search process. The adoption of rigid learning pattern and communication topology may restrict the intelligence level of each particle, hence degrad...
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8515194/ |
| _version_ | 1818379689397321728 |
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| author | Wei Hong Lim Nor Ashidi Mat Isa Sew Sun Tiang Teng Hwang Tan Elango Natarajan Chin Hong Wong Jing Rui Tang |
| author_facet | Wei Hong Lim Nor Ashidi Mat Isa Sew Sun Tiang Teng Hwang Tan Elango Natarajan Chin Hong Wong Jing Rui Tang |
| author_sort | Wei Hong Lim |
| collection | DOAJ |
| description | Most existing particle swarm optimization (PSO) variants use a single learning strategy and a fixed neighborhood structure for all particles during the search process. The adoption of rigid learning pattern and communication topology may restrict the intelligence level of each particle, hence degrading the performance of PSO in solving the optimization problems with complicated fitness landscapes. Recent studies suggested that the employment of self-adaptive mechanism in adjusting the search strategy and topology connectivity of each particle along the search process may serve as a potential remedy to improve the performance of PSO, especially when dealing with complex problems. For this reason, a self-adaptive topologically connected (SATC)-based PSO equipped with an SATC module and an improved learning framework is proposed. The SATC module is envisioned to facilitate each particle to perform searching with different exploration and exploitation strengths by adaptively modifying its topology connectivity in different searching stages. A modified velocity update scheme and an alternative search operator are also introduced to formulate an improved learning framework to enhance the performance of proposed work further. Substantial numbers of benchmark functions and two real-world optimization problems are used to compare SATC-based PSO (SATCPSO) with several well-established PSO variants. Extensive studies have verified that SATCPSO is more competitive than its peers in most of the tested problems. |
| first_indexed | 2024-12-14T02:06:47Z |
| format | Article |
| id | doaj.art-191a152bbfc04563b1b75d55be6fd0e0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:06:47Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-191a152bbfc04563b1b75d55be6fd0e02022-12-21T23:20:53ZengIEEEIEEE Access2169-35362018-01-016653476536610.1109/ACCESS.2018.28788058515194A Self-Adaptive Topologically Connected-Based Particle Swarm OptimizationWei Hong Lim0https://orcid.org/0000-0003-1673-8088Nor Ashidi Mat Isa1https://orcid.org/0000-0002-2675-4914Sew Sun Tiang2Teng Hwang Tan3Elango Natarajan4https://orcid.org/0000-0003-1215-0789Chin Hong Wong5https://orcid.org/0000-0002-6343-7635Jing Rui Tang6https://orcid.org/0000-0002-1806-5086Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaDepartment of Engineering and Information Technology, UCSI University, Kuala Lumpur, MalaysiaFaculty of Technical and Vocational Education, Universiti Pendidikan Sultan Idris, Tanjung Malim, MalaysiaMost existing particle swarm optimization (PSO) variants use a single learning strategy and a fixed neighborhood structure for all particles during the search process. The adoption of rigid learning pattern and communication topology may restrict the intelligence level of each particle, hence degrading the performance of PSO in solving the optimization problems with complicated fitness landscapes. Recent studies suggested that the employment of self-adaptive mechanism in adjusting the search strategy and topology connectivity of each particle along the search process may serve as a potential remedy to improve the performance of PSO, especially when dealing with complex problems. For this reason, a self-adaptive topologically connected (SATC)-based PSO equipped with an SATC module and an improved learning framework is proposed. The SATC module is envisioned to facilitate each particle to perform searching with different exploration and exploitation strengths by adaptively modifying its topology connectivity in different searching stages. A modified velocity update scheme and an alternative search operator are also introduced to formulate an improved learning framework to enhance the performance of proposed work further. Substantial numbers of benchmark functions and two real-world optimization problems are used to compare SATC-based PSO (SATCPSO) with several well-established PSO variants. Extensive studies have verified that SATCPSO is more competitive than its peers in most of the tested problems.https://ieeexplore.ieee.org/document/8515194/Alternative search operatorglobal optimizationimproved learning frameworkparticle swarm optimizationself-adaptivetopology connectivity adaptation |
| spellingShingle | Wei Hong Lim Nor Ashidi Mat Isa Sew Sun Tiang Teng Hwang Tan Elango Natarajan Chin Hong Wong Jing Rui Tang A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization IEEE Access Alternative search operator global optimization improved learning framework particle swarm optimization self-adaptive topology connectivity adaptation |
| title | A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization |
| title_full | A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization |
| title_fullStr | A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization |
| title_full_unstemmed | A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization |
| title_short | A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization |
| title_sort | self adaptive topologically connected based particle swarm optimization |
| topic | Alternative search operator global optimization improved learning framework particle swarm optimization self-adaptive topology connectivity adaptation |
| url | https://ieeexplore.ieee.org/document/8515194/ |
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