一种基于人工鱼群和文化算法的新型混合全局优化算法

Hybrid global optimization algorithm based on artificial fish swarm and cultural algorithm

提出一种基于人工鱼群和文化算法的新型混合全局优化算法,该混合算法的思想是将人工鱼群嵌入文化算法框架中,作为种群空间的一个进化过程;通过从进化种群中获得的知识组成知识空间,两空间具有各自群体并独立并行演化,从而实现增加人工鱼群的群体多样性。最后通过数值实例仿真结果表明,本算法具有较高的计算精度和收敛速度。

This paper proposed a hybrid global optimization algorithm based on artificial fish swarm and cultural algorithm. The algorithm embedded in the cultural algorithm framework consisted of an AFSA-based main population space and a knowledge spec from the evolving population, which respectively had its own population to evolve independently and parallel. The mechanism improved the population diversity. Finally by comparison computed result of the example, it can be found that this proposed algorithm illustrates its higher the computational accuracy and convergence rate.