基于两阶段策略的粒子群优化研究

提出了一种基于传统粒子群优化的两阶段实施方案，通过对一组测试函数的仿真表明，该方案以适当增加的计算量为代价，提高了搜索成功率. 对比实验表明，两阶段方案几乎在各种最大可迭代次数的约束下都能获得更好的搜索成功率，且对学习速度参数的敏感性降低，算法的搜索性能更稳健.实施该策略时原则上子群数量宜选取一个适中的数值，以综合考虑可靠性与计算成本两个因素.

Research of Particle Swarm Optimization Based on a Two-Stage Strategy

A two-stage implementation strategy based on canonical particle swarm optimization was proposed. With the cost of acceptably additional evaluations, this strategy achieved higher success rate which were demonstrated by a suite of benchmark functions. The simulation showed that two-stage implementation strategy could bring forward relatively higher success rate under different upper limita- tion of iterations. At the same time the proposed strategy reduced the sensitivity of learning rate and presented a stable performance. It was revealed experimentally that the number of sub-populations should be set at a moderate value to consider both the reliability and the computation cost.