Hybrid-search quantum-behaved particle swarm optimization algorithm
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 quantumbehaved 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 hybridsearch 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.
quantum-behaved particle swarm optimization(QPSO) chaos serch Tent Map particle swarm optimization(PSO)
Zhou Chao Sun Jun
Institute of IOT engineering Southern Yangtze University Wuxi,China
国际会议
无锡
英文
319-323
2011-10-14（万方平台首次上网日期，不代表论文的发表时间）