工程应用中的多峰寻优问题要求搜索目标函数的多个极值点，现有的多峰优化方法难以直接利用应用

问题的先验知识引导算法过程，多峰寻优效率较低。基于粒子群优化算法设计一种面向应用的多峰寻优算法，

能有效利用易于获得的先验参数，如峰间分辨率、峰位置精度、峰值个数等实现快速多峰搜索。该算法保持了粒

子群算法的简单性并改善了搜索多样性，使其可控地收敛到多个峰值上。将该算法与几种典型的多峰寻优方法

进行了对比测试和分析，结果表明，对复杂多峰函数，该算法能以最快的收敛速度实现多峰搜索-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