摘要： 基本人工鱼群算法采用基于距离的邻域拓扑结构，存在计算量大、运行速度慢等问题。为此，引入粒子群优化算法中的 4种典型种群拓扑结构：星形，轮形，环形和冯?诺依曼结构，代替基于距离的邻域拓扑结构，并分析不同构对算法性能的影响。在5个准测试函数上的实验结果表明，对于单峰函数，星形结构算法的优化效果较好；对于局部最优点较多的函数，轮形和环形结构算法的优化效果较好。根据优化问题的复杂度选用不同的拓扑结构，可以提高人工鱼群算法的优化性能。
关键词:
人工鱼群算法,
群体智能,
人工智能,
种群,
拓扑结构,
邻域,
收敛速度,
优化性能
Abstract: Distance-based neighborhood topology, which will lead to large amount of calculation and slow speed and so on, is used by the basic Artificial Fish Swarm Algorithm(AFSA). Aiming at this problem, four typical population topologies(star topology, wheel topology, circle topology and John von Neumann topology) instead of distance-based neighborhood topology are introduced, and the effects of different neighborhood topology on the AFSA performance are analyzed. Experimental results in five functions show that the star topology is more suitable for unimodal function, wheel topology and circle topology are better for the functions of many local optimal points. And choosing appropriate topology for complexity of optimization problems can improve the optimization performances of AFSA.
Key words:
Artificial Fish Swarm Algorithm(AFSA),
swarm intelligence,
artificial intelligence,
population,
topology,
neighborhood,
convergence speed,
optimization performance
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