一种随机蚁群算法求解连续空间优化问题  

Stochastic ant colony algorithm for continuous space optimization

作　　者：寇晓丽[1] 刘三阳[1] 张建科[2] 

机构地区：[1]西安电子科技大学数学科学系,陕西西安710071 [2]西安邮电学院应用数理系,陕西西安710121

出　　处：《系统工程与电子技术》2006年第12期1909-1911,共3页Systems Engineering and Electronics

基　　金：国家自然科学基金(69972036);陕西省自然科学基金(2000SL03)资助课题

摘　　要：通过将蚁群优化算法(ant colony optimization,ACO)与一种随机优化方法———Alopex算法相结合,提出一种随机蚁群混合算法(AACO)求解连续空间优化问题。该算法定义了蚁群在连续空间中的寻优方式以及新的信息素更新规则,并在局部搜索过程中嵌入改进的Alopex算法以提高搜索效率,有效地避免了优化算法陷入局部最优。基于多极值函数和非线性连续函数的仿真实验表明,该算法简单高效,具有良好的寻优性能。A stochastic optimization algorithm, AACO, is proposed by combining ant colony optimization （ACO） algorithm with Alopex algorithm that is a stochastic optimization method for solving continuous space optimization problems. The proposed approach defines the ants＇ searching way in continuous space and the new pheromone updating rule. Also, Alopex algorithm is embedded into the local search process to improve the search capability and avoid being trapped in local optimum. Simulation results on benchmark multi-minimum functions and nonlinear continuous functions show that the algorithm is effective, efficient and has a good search performance.

关 键 词：蚁群优化 随机优化方法 ALOPEX算法 连续空间优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]