Many scientific and engineering applications involve finding more than one optimum. A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of the existing methods was also provided. Several techniques in solving multimodal function optimization problems were introduced, such as clearing, deterministic crowding, sharing, species conserving and so on. And we summarized defects of existing algorithms: lacking of self-adaptive adjustment function, requiring setting some parameters according to different problems, lacking of unified theoretical and experimental system to guide algorithms design and not maintaining the diversity of swarm. Moreover, most of existing multimodal particle swarm optimization algorithms which include SPSO, MSPSO, ESPSO, ANPSO, kPSO, MGPSO, AT-MGPSO, rpso, and SDD-PSO were described and compared and advantages and disadvantages existing in these algorithms were pointed out. Therefore, some ideas to improve the performance of multimodal function optimization algorithms were proposed

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