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Dynamic Population Cooperative: Particle Swarm Optimization for Global Optimization Problems

Wei Li, Cisong Shi, Qing Xu, Ying Huang

Source Title: International Journal of Swarm Intelligence Research (IJSIR) 13(1)

DOI: 10.4018/IJSIR.313664

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Abstract

Particle swarm optimization (PSO) has attracted wide attention in the recent decade. Although PSO is an efficient and simple evolutionary algorithm and has been successfully applied to solve optimization problems in many real-world fields, premature maturation and poor local search capability remain two critical issues for PSO. Therefore, to alleviate these disadvantages, a dynamic population cooperative particle swarm optimization for global optimization problems (DPCPSO) is proposed. Firstly, to enhance local search capability, an elite neighborhood learning strategy is constructed by leveraging information from elite particles. Meanwhile, to make the particle easily jump out of the local optimum, a crossover-mutation mechanism is utilized. Finally, a dynamic population partitioning mechanism is designed to balance exploration and exploitation capabilities. 16 classic benchmark functions and 1 real-world optimization problem are used to test the proposed algorithm against with 6 typical PSO algorithms. The experimental results show that DPCPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of DPCPSO are also significantly improved. Therefore, the algorithm is highly competitive in solving global optimization problems.

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In standard PSO, each particle has its own velocity and position. The velocity is guided by the global optimal particle and the individual historical optimal particle, it determines the direction and distance the particle position moves. The position of each particle means one of the solutions. In the iterative process, the ith particle updates its velocity and position according to Eq (1) and Eq (2).