Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

Qiang Yang, Litao Hua, Xudong Gao, Dongdong Xu, Zhenyu 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
Litao Hua: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xudong Gao: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dongdong Xu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Zhenyu 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 5, 1-34

Abstract: Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

Keywords: stochastic cognitive dominance leading; multimodal problems; particle swarm optimization; global optimization; evolutionary algorithm (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 (6)

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