Abstract— Quantum-behaved particle swarm optimization (QPSO) algorithm has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a new algorithm, called DCSQPSO, which employs a diversity-controlled into QPSO enhancing mechanism and local search strategies to improve the solution quality. A comprehensive experimental study is conducted on a set of benchmark functions, Comparison results show that DCSQPSO obtains a promising performance on the majority of the test problems.
Index Terms— Quantum-behaved particle swarm optimization, diversity-controlled, local search, global optimization.
The authors are with School of Information Science Technology, Hainan Normal University, Haikou 571158, Hainan, China (e-mail: haixia_long@163.com, 595615374@qq.com, 605515770@qq.com).
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Cite: Haixia Long, Shulei Wu, and Chun Shi, " Diversity-Controlled Quantum-Behaved Particle Swarm Optimization Based on Local Search," International Journal of Information and Electronics Engineering vol. 5, no. 2, pp. 131-137, 2015.
