Quantum-behaved particle swarm optimization with dynamic grouping searching strategy

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.

Automated summary

This paper proposes a hybrid quantum-behaved particle swarm optimization algorithm (QPSO-DGS) with dynamic grouping searching strategy, which aims to solve the premature convergence issue in complex optimization problems. Experimental results show that QPSO-DGS has promising performance in terms of solution accuracy and convergence speed, especially on multimodal problems.