Adaptive niche quantum evolutionary algorithm for multimodal function

Since it is difficult to find all the global and local optimal solutions in multimodal optimization problem for quantum evolutionary algorithm which can only find a global optimal solution, an adaptive niche quantum evolutionary algorithm is proposed. A good-point set is used to produce the initial population which is scattered uniformly over the entire search space. An adaptive niche identification method based on topographic center is designed to identify the extremum areas of the population adaptively, and a strategy of niche integrity is presented to increase the niche identification speed. The fast optimization ability of quantum evolutionary algorithm is applied to search extrema precisely. The strategy of dynamic population has been used to maintain diversity of population, and adjust the size of population adaptively. Simulation results show that the proposed algorithm has good glabal optimization performance and local extremum search ability and solutions are satisfactory.