Affiliations: [a] School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China | [b] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China | [c] Centre for Computational Science and Mathematical Modeling, Coventry University, Coventry, UK
Correspondence:
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Corresponding author: Jun Sun, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China. E-mail: [email protected].
Abstract: The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.
