An adaptive niche genetic algorithm for multimodal function optimization

An adaptive niche genetic algorithm is proposed to solve the problems of the inaccurate niche identification and the conflict between quick convergence and population diversity maintaining in niche genetic algorithms. In the proposed algorithm, an improved niche identification method is designed to identify the niches of the population, and a concept of niche entropy is introduced to measure the diversity of the population. The evolutionary parameters of the algorithm can be adjusted adaptively on the basis of the niche entropy of population. And the strategies of selection and crossover are also improved in the algorithm. The strategies divide the operation of crossover into the out-niche crossover and the in-niche crossover to enhance global searching ability and local convergence rate of the algorithm. Experimental results show that the proposed algorithm can solve the multimodal function optimization problems with quick convergence rate, low computational complexity and avoidance of the genetic drift.