Research on adaptive period mutation-based QPSO algorithm
自适应阶段变异量子粒子群优化算法研究

The standard quantum particle swarm optimization (SQPSO) algorithm may sink into local optimum. To overcome this shortcoming, this paper introduced the mutation mechanism. Based on the concept of evolution period, it proposed adaptive period mutation-based QPSO algorithms(APMQPSOs). It used four kinds of mutation probability decreasing methods to periodically mutate global best position with cauchy random numbers in QPSO algorithm, thus formed four different APMQPSO algorithms. It adopted five typical test functions to conduct simulation experiment, and compared experimental results of four APMQPSOs and SQPSO with each other. The experiment results show that APMQPSOs with linear variation mutation probability are effective for unimodal function optimization problems, while algorithms with nonlinear variation mutation probability have very strong optimization abilities for multimodal ones.