多目标觅食—返巢机制连续域蚁群算法  

Multi-objective function optimization based on ant colony algorithm with foraging-homing mechanism

作　　者：金浩[1] 刘维宁[1] 

机构地区：[1]北京交通大学土木建筑工程学院,北京100044

出　　处：《计算机应用研究》2012年第11期4038-4040,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(51008017);中央高校基本科研业务费专项资金资助项目(2012YJS072)

摘　　要：受自然界蚂蚁的觅食—返巢生物学特征启发,同时深入了解蚂蚁信息素成分,提出了一种能够解决函数多目标优化问题的改进蚁群算法——多目标觅食—返巢机制连续域蚁群算法(MO-FHACO)。该算法与传统蚁群算法相比,将信息素分为蚁巢信息素和食物信息素,并根据不同信息素设立了不同的释放和寻优机制。通过BNH和TNK问题验证,MO-FHACO算法在Pareto最优前端连续的情况下具有极佳的多目标优化能力;在Pa-reto最优前端不连续的情况下,也能得到较多且散布性较好的Pareto最优解。因此,MO-FHACO算法是一种有效的函数多目标优化算法。For extending the ability of multi-objective optimization for continuous functions for the ant colony algorithm,this paper proposed an improved ant colony algorithm（MO-FHACO） based on the foraging-homing mechanism inspired by the natural ant colonies who laid the different pheromones.The pheromones were divided into two kinds,i.e.the nest pheromone and the food pheromone,on the path from the nest to the food resource.Therefore,it built the foraging-homing mechanism to find the function optimal value.According to the function test of BNH and TNK,results show that MO-FHACO has the best multi-objective function optimization ability comparison with other intelligence algorithms,if Pareto frontier is continuous.And if Pareto frontier is discontinuous,MO-FHACO still can get good Pareto optimum values.So MO-FHACO is an efficient multi-objective function optimization algorithm.

关 键 词：蚁群算法 连续函数 多目标优化 觅食—返巢机制 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]