Diversity control based on distribution entropy in population-based search and optimization

A quantitative description of diversity in population-based search algorithms is put forward by comparing distribution entropy with variance. The problem of mode classification in individual space is presented for multimodal cases in optimization computation, and a classification method is proposed. On the basis of clustering analysis, the class distribution of individuals in search space is acquired. Furthermore, the diversity index described by distribution entropy is obtained. Then, diversity control is implemented by aggregation and dilation among individuals according to diversity. As an example, a first-order aggregation and dilation (A&D) algorithm for diversity control is presented and the setting of its parameters is analyzed. Simulation results demonstrate that the proposed algorithm performs better than the canonical genetic algorithm, the particle swarm optimization and the A&D search algorithm without classification.