基于反向学习的自组织迁移算法

Opposition-based Self-organizing Migrating Algorithm

为了解决自组织迁移算法存在的早期收敛问题,提出了基于反向学习的自组织迁移算法(Opposition-basedSelf-organizing Migrating Algorithm,OSOMA)。该算法利用反向学习机制扩展了个体的搜索方向,获得了更优秀的采样个体,使得算法在保持多样性的同时提高了收敛速度。此外,该算法还对步长进行自适应调整,进一步平衡了算法的勘探和开采能力。通过典型函数的测试证实了OSOMA的有效性。

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 effectiveness of OSOMA.