基于信息熵和混沌理论的遗传-蚁群协同优化算法  

Genetic and ant colony collaborative optimization algorithm based on information entropy and chaos theory

作　　者：薛锋[1] 王慈光[1] 牟峰[1] 

机构地区：[1]西南交通大学交通运输学院,成都610031

出　　处：《控制与决策》2011年第1期44-48,共5页Control and Decision

基　　金：国家自然科学基金项目(60776824);中央高校基本科研业务费专项资金项目(SWJTU09BR134);西南交通大学青年教师科研起步项目(2009Q040)

摘　　要：为了融合遗传算法和蚁群算法在解决组合优化问题方面的优势,提出一种基于信息熵和混沌理论的遗传-蚁群协同优化算法.利用信息熵产生初始群体,增加初始群体的多样性,并将混沌优化的遍历特性引入融合的遗传-蚁群算法,改进相关参数,实现参数的自适应控制以及遗传算法与蚁群算法混合优化策略的有机集成.通过仿真实例表明了混合智能算法在解决旅行商问题(TSP)50座城市最短路径寻优时的有效性.In order to merge the advantage of genetic algorithm and ant colony algorithm in solving combinatorial optimization problem, a kind of genetic and ant colony collaborative optimization algorithm based on information entropy and chaos theory is proposed. This algorithm produces the initial colony by information entropy to increase the variety of the initial colony, and introduces the traversal characteristic of chaos optimization to the integrated genetic and ant colony algorithm. Some relevant parameters in the algorithm are improved, the adaptive control of the parameters is realized, and the hybrid optimization strategy of genetic algorithm and ant colony algorithm is integrated. Simulation example shows that this hybrid intelligent algorithm is valid in solving traveling salesperson problem of 50 cities.

关 键 词：信息熵 混沌映射函数 遗传算法 蚁群算法 协同优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]