摘要
针对人工鱼群算法局部搜索不精确、微粒群优化算法易发生过早收敛等问题,提出一种新的人工鱼群与微粒群混合优化算法。算法的主要思想是先利用人工鱼群的全局收敛性快速寻找到满意的解域,再利用粒子群算法进行快速的局部搜索,所得混合算法具有局部搜索速度快,而且具有全局收敛性能。最后,以五个标准函数和一个应用实例进行测试,测试结果表明,提出的算法在一定程度上避免了陷入局部极小,加快了收敛速度且提高了搜索精度。
The artificial fish swarm algorithm （AFSA） has a stronger robustness, and has a imprecision of solution. The particle swarm optimization algorithm （PSO） is simple and effective, and is easy in premature convergence. This paper presented a new hybrid evolutionary algorithm with artificial fish swarm algorithm and particle swarm optimization. This algorithm has the advantages of both AFSA and PSO. First, algorithm looked for satisfactory solution space with AFSA, later its search exacted solution with PSO. By doing experiments on five benchmark functions and a applicable examples, the results show that the algorithm avoids trapping into local optimum in a certain extent and improves the precision of convergence.
出处
《计算机应用研究》
CSCD
北大核心
2010年第6期2084-2086,2102,共4页
Application Research of Computers
基金
广西自然科学基金资助项目(0832082
0991086)
国家民委科研基金资助项目(08GX01)
关键词
微粒群算法
人工鱼群算法
混合算法
测试函数
particle swarm optimization
artificial fish swarm algorithm
hybrid algorithm
test functions
