Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization

Qiang Yang, Yu-Wei Bian, Xu-Dong Gao, Dong-Dong Xu, Zhen-Yu Lu, Sang-Woon Jeon and Jun Zhang
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Qiang Yang: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yu-Wei Bian: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xu-Dong Gao: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dong-Dong Xu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Zhen-Yu Lu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Sang-Woon Jeon: Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Korea
Jun Zhang: Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Korea

Mathematics, 2022, vol. 10, issue 7, 1-39

Abstract: 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.

Keywords: particle swarm optimization; stochastic triad topology; guiding exemplar; multimodal problems; global optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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