多模态函数优化的多种群进化策略

在一种使用单基因变异、精英繁殖、递减型策略参数的改进进化策略基础上，提出了一种求解多模态函数多个极值点的多种群协同进化策略，并给出了子种群进化概率、停止条件的确定和收敛到极值点的判断条件,在求多极值点的进化算法中，判别两个极值点是同峰还是异峰极值点是一个困难而关键的问题，为此引入了一种新的判别方法——山谷探索法，从而避免了确定小生境单径或峰半径,一组测试函数的仿真计算结果表明了所提出的算法能准确地找到全部极值点.

Multi-population Evolution Strategies for Multi-modal Function Optimization

Based on improved evolution strategies with single-gene mutation, elitist reproduction, and descending strategy parameters, a cooperating multi-population evolution strategy is proposed for the optimization of multi-modal function. The subpopulation's probability of evolution, stopping condition, and criterion of convergence to local optima are presented. A new method named as valley searching to distinguish between same-peak optima and different-peak ones is proposed, and determining the radius of niche or peak is avoided which has been a hard problem for fitness sharing genetic algorithms. Simulation results on a set of benchmark functions show that the algorithm can properly find all the optima.