Introduction - If you have any usage issues, please Google them yourself
Multimodal optimization problem in engineering applications require more extreme points of the search objective function the existing multimodal optimization method is difficult to directly use the application of a priori knowledge to guide the algorithm process less efficient multimodal optimization. Based on particle swarm optimization algorithm to design an application-oriented multimodal optimization algorithm can effectively take advantage of the easy access to the a priori parameters, such as the resolution of the peak-to-peak, peak position accuracy, the number of peaks to achieve fast multimodal search. The algorithm keeps controllable particle swarm algorithm simple and to improve the search for diversity, it converges to multiple peaks. This algorithm with several typical multimodal optimization method comparison test and analysis results show that the complex multimodal function, the algorithm can achieve the fastest convergence rate multimodal search