蜜蜂进化遗传与捕鱼策略相结合的优化算法  

Optimal Algorithm Combining Bee Evolution Genetic and Fishing Strategy

作　　者：梁晓龙[1] 李祚泳[1] 汪嘉杨[1] 

机构地区：[1]成都信息工程大学资源环境学院,四川成都610225

出　　处：《数学的实践与认识》2016年第17期143-148,共6页Mathematics in Practice and Theory

基　　金：国家自然科学基金(51209024)

摘　　要：针对捕鱼策略优化算法未充分利用群体最优个体信息因而收敛速度较慢的缺陷,提出了将蜜蜂进化遗传算法与捕鱼策略相结合的混合优化算法.算法将蜂王具有最优遗传基因的特点引入到渔夫撒网捕鱼策略中,能较好利用群体当前最优个体的信息,提高搜索速率;并保留捕鱼策略中渔夫移动搜索策略的独立性,避免陷入不成熟收敛.通过对多个典型测试函数的测试表明:蜜蜂进化遗传算法与捕鱼策略相结合的优化算法,比简单的捕鱼策略的优化算法在寻优能力、稳定性和收敛速度等方面均有提高.Considering the slower convergence of fishing strategy algorithm for lack of the full use of the best individual information in the group,a new composite optimal algorithm combining bee evolution genetic and fishing strategy optimization algorithm（BSGA-FSOA）was proposed.The proposed algorithm integrated the characteristics of the optimal heredity gene of bee queen into the fishing strategy.The algorithm can use information of the current best individual in the population,so as to improve search speed.At the same time,the algorithm can avoid premature convergence due to the independence of keep fishing strategy of moving search strategy.This algorithm was tested by typical test functions,the results show that the optimal algorithm combining bee evolution genetic and fishing strategy has better optimal searching ability and stability as well as faster convergence than those of simple fishing strategy optimization algorithm.

关 键 词：捕鱼策略 蜜蜂进化遗传算法 测试函数 函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]