多峰函数优化的改进人工鱼群混合算法  

Improved artificial fish swarm mixed algorithm for multimodal function optimization

作　　者：邓涛[1] 姚宏[2] 杜军[1] 

机构地区：[1]空军工程大学航空航天工程学院,西安710051 [2]空军工程大学理学院,西安710038

出　　处：《计算机应用》2012年第10期2904-2906,2910,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51175510);航空科学基金资助项目(20111396011);空军工程大学研究生创新基金资助项目

摘　　要：针对人工鱼群算法(AFSA)应用于多峰优化问题时搜索能力不足、优化精度不高的缺点,提出了一种改进的人工鱼群混合算法。该算法中,采用优胜劣汰抑制策略,筛选出精英人工鱼群;对聚群行为和追尾行为进行寻优,有利于人工鱼在新的寻优轨迹上进行仔细搜索;对觅食行为进行了改进,避免人工鱼陷入平坦位置;结合模式搜索法,增强其局部精细搜索能力。仿真结果表明,所提出的算法具有较强全局优化能力和局部优化能力,搜索到每个最优解精度都达到了理想值,且能够用于复杂多峰函数优化。In order to deal with the problems of inefficient searching and low accuracy of Artificial Fish Swarm Algorithm （AFSA） for multimodal function optimization, an improved AFSA for muhimodal function optimization was proposed. In the algorithm, the strategy of the survival of the fitter suppression was adopted, eliminating artificial fish which was situated in food with low concentration of similar artificial fish to select elite artificial fish swarm. Optimization for swarming behavior and following behavior contributed to artificial fish careful search in a new optimization trajectory to enhance its local search capacity. Modifying for preying behavior, artificial fish was avoided sinking flat position. In combination with Pattern Search Method （ PSM）, its local accuracy search capacity was enhanced. The simulation results indicate that the proposed algorithm has stronger global optimization and local optimization capabilities, and the search for each optimal solution accuracy has reached the ideal value, and it is able to be used for complex muhimodal function optimization.

关 键 词：人工鱼群算法 多峰优化 模式搜索法 优胜劣汰 聚群行为 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]