多目标优化问题的蚁群算法研究  

On ant colony algorithm for solving multiobjective optimization problems

作　　者：张勇德[1] 黄莎白[1] 

机构地区：[1]中国科学院沈阳自动化研究所,辽宁沈阳110015

出　　处：《控制与决策》2005年第2期170-173,178,共5页Control and Decision

摘　　要：将离散空间问题求解的蚁群算法引入连续空间,针对多目标优化问题的特点,提出一种用于求解带有约束条件的多目标函数优化问题的蚁群算法.该方法定义了连续空间中信息量的留存方式和蚂蚁的行走策略,并将信息素交流和基于全局最优经验指导两种寻优方式相结合,用以加速算法收敛和维持群体的多样性.通过3组基准函数来测试算法性能,并与NSGAII算法进行了仿真比较.实验表明该方法搜索效率高,向真实Pareto前沿逼近的效果好,获得的解的散布范围广,是一种求解多目标优化问题的有效方法.The ant colony algorithm (ACA) that is often applied to discrete space optimization problems is introduced into continuous space to solve constrained multiobjective optimization problems. The new pheromone remaining (process) and walking strategy of ants are described. In addition, combined with the searching strategy based on (global) best experience, this approach guides the ants to search better solutions. The approach is validated using (several) benchmark cases. The simulation results show that the approach possesses high searching efficiency and can efficiently find multiple Pareto optimal solutions.

关 键 词：蚁群算法 约束多目标优化 连续空间寻优 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]