Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization
Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of t...
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MDPI AG
2022-03-01
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| author | Qiang Yang Yu-Wei Bian Xu-Dong Gao Dong-Dong Xu Zhen-Yu Lu Sang-Woon Jeon Jun Zhang |
| author_facet | Qiang Yang Yu-Wei Bian Xu-Dong Gao Dong-Dong Xu Zhen-Yu Lu Sang-Woon Jeon Jun Zhang |
| author_sort | Qiang Yang |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants. |
| first_indexed | 2024-03-09T11:39:07Z |
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| language | English |
| last_indexed | 2024-03-09T11:39:07Z |
| publishDate | 2022-03-01 |
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| series | Mathematics |
| spelling | doaj.art-5216266bed2a48e49b81d9dead2b551e2023-11-30T23:36:10ZengMDPI AGMathematics2227-73902022-03-01107103210.3390/math10071032Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical OptimizationQiang Yang0Yu-Wei Bian1Xu-Dong Gao2Dong-Dong Xu3Zhen-Yu Lu4Sang-Woon Jeon5Jun Zhang6School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaParticle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants.https://www.mdpi.com/2227-7390/10/7/1032particle swarm optimizationstochastic triad topologyguiding exemplarmultimodal problemsglobal optimization |
| spellingShingle | Qiang Yang Yu-Wei Bian Xu-Dong Gao Dong-Dong Xu Zhen-Yu Lu Sang-Woon Jeon Jun Zhang Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization Mathematics particle swarm optimization stochastic triad topology guiding exemplar multimodal problems global optimization |
| title | Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization |
| title_full | Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization |
| title_fullStr | Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization |
| title_full_unstemmed | Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization |
| title_short | Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization |
| title_sort | stochastic triad topology based particle swarm optimization for global numerical optimization |
| topic | particle swarm optimization stochastic triad topology guiding exemplar multimodal problems global optimization |
| url | https://www.mdpi.com/2227-7390/10/7/1032 |
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