双种群分子动理论优化算法

A dual population based molecular kinetic theory optimization algorithm

针对传统分子动理论优化算法存在寻优精度差、易陷入局部极值等不足,提出了一种双种群分子动理论优化算法。该算法将种群分为精英和普通两个子群:普通子群采用传统分子动理论优化算法搜索策略进行大范围搜索,而精英子群则通过协同合作实现精细化搜索,以提高算法收敛精度;基于个体迁移实现子群间的信息交流,两个子群通过分工合作共同完成搜索过程。实验结果表明:改进算法在收敛速度、精度和算法稳定性等方面都有明显改善。

The molecular kinetic theory optimization algorithm has the disadvantages of poor optimization accuracy and ease of being stuck into local extremum.A new molecular kinetic theory optimization algorithm with two populations is proposed.In this algorithm,the whole population is divided into two sub-groups:the elite subgroup and the ordinary subgroup.The ordinary subgroup uses the search strategy of traditional molecular kinetic theory optimization algorithm to do a wide range search.Through cooperation,the elite subgroup does a refinement search to improve the convergence accuracy of the algorithm.Information exchange among sub-groups is completed through individual migration.Two sub-groups complete the search process by cooperation.Test results show that the improved algorithm significantly improves the convergence speed,accuracy and stability.