Opposition-based Self-organizing Migrating Algorithm
基于反向学习的自组织迁移算法

A new opposition-based self-organizing migrating algorithm(OSOMA) was proposed to deal with premature convergence of self-organizing migrating algorithm. The key points of OSOMA lie in;1) the opposition-based learning is applied to extend the migrating direction and obtain better individual, which maintains diversity of population and improves the convergence speed. 2) the algorithm adaptively adjusts the step to further balance between the ability of exploration and exploitation capacity. Then, OSOMA is used to solve typical problems and numerical results show the effcctivcncss of OSOMA.