Diversity-Controlled Quantum-Behaved Particle Swarm Optimization Based on Local Search
Keywords:
Quantum-behaved particle swarm optimization, diversity-controlled, local search, global optimization.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.
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