Hybrid-Search Quantum-Behaved Particle Swarm Optimization Algorithm

摘要

Quantum-behaved particle swarm optimizafion algorithm(QPSO) can improve the search quality of particle swarm optimizafion algorithm(PSO) in a certain extent. But it still shows that its precision of searching is low and its capability of local searching is weak. Hybrid-search quantum-behaved particle swarm optimizafion algorithm(HSQPSO) has introduced the Chaos search mechanism which based on tent map. It doesn't change the search mechanism of QPSO, and it re-joins the chaos search mechanism to compose the hybrid-search mechanism based on the original. Through comparing the optimal values of two search mechanisms in the iterative process, the global optimum will be obtained. results show that the HSQPSO not only retains the fast convergence of QPSO, but also has higher search efficiency and search precision and isn't easy to be trapped in the local optimal value.