Multimodal function optimization using a crowding differential evolution

This paper presents a crowding differential evolution(CDE) algorithm applied to multimodal function optimization for finding all the extreme solutions,using DE's(differential evolution,DE) global search strategy and internal parallel pattern.The high crowding factor(CF) value search avoids the replacement error,maintains diversity of species,and can accurately locate all the multimodal function's optimal solutions and extreme solutions.Meanwhile,this algorithm has a lot of advantages such as less parameters,simple operator and swift convergence rate.The algorithm is compared with crowding genetic algorithm and simulation experiment results show that crowding differential evolution is better than crowding genetic algorithm(CGA) in both convergence rate and convergence accuracy.