Multi-peaks function optimization using quantum-behaved particle swarm optimization
一种求解多峰函数优化问题的量子行为粒子群算法

An improved Quantum-behaved Particle Swarm Optimization (QPSO) for multi-peaks functions optimization was proposed. In the proposed Species-based QPSO (SQPSO), the swarm population was divided into subpopulations in parallel based on their similarity. Each subpopulation was set around a dominating particle called the species seed. Over successive iterations, species were able to simultaneously optimize towards multiple optima by using QPSO, so each peaks were ensure to be searched equally, no matter whether they are global or local optima. Experimental results demonstrate that SQPSO can search as many peaks of function as possible. Simulation results show the convergence performance of SQPSO is superior to that of PSO.