基于动态群体的聚集演化求解多峰函数优化问题

指出了现有的演化算法框架都是群体固定的演化迭代过程，对求解多峰函数优化问题时由于无法事先得知峰值点的个数而很难确定合适的群体大小，影响了算法的效率，提出了一种群体动态可调的演化方式，使得初始群体大小可任意指定，在演化过程中通过聚集和按比例引入新个体两个过程而动态变化。实验表明，该算法能尽可能多地定位峰值点。

Novel Dynamic-Population Based on Evolutionary Algorithm for Multimodal Function Optimization

The traditional evolutionary algorithm with a fixed size population is not suitable especially for solving multimodal function optimization because it's impossible to know the number of solution in advance and hence it's difficult to specify a suitable size of population. In this paper, a novel algorithm with dynamic population is presented. In the process of evolution, the size of population is tuned by a aggregation and introduction of new individuals. An initial experiment is given.