增强型的蚁群优化算法  

An Improved Ant Colony Optimization

作　　者：燕忠[1] 袁春伟[1] 

机构地区：[1]东南大学生物医学工程系,南京210096

出　　处：《计算机工程与应用》2003年第23期62-64,共3页Computer Engineering and Applications

基　　金：国家自然科学基金重点项目(编号:69831010)资助

摘　　要：旅行商问题是一个NP-Hard组合优化问题。根据蚁群优化算法和旅行商问题的特点,论文提出了对蚁群中具有优质解的蚂蚁个体所走路径上的信息素强度进行增强的方法,并同其他的优化算法进行了比较,仿真结果表明,对具有全局和局部最优解的个体所走路径上的信息素强度进行增强的蚁群优化算法比标准的蚁群优化算法和其他优化算法在执行效率和稳定性上要高。Traveling Salesman Problem(TSP)is a NP-Hard combinatorial optimization problem.According to the merits of Ant Colony Optimization(ACO)and the characters of TSP,methods are proposed to make ACO run efficiently by modi-fying and strengthening the pheromone intensity of the ants with the best solutions in the current or global ant colonies,and are compared with other evolutionary algorithms such as Genetic algorithm,Simulated Annealing.The experi-ment results prove that the improved ACOs are more efficient than the standard ACO and superior to other evolution-ary algorithms.Moreover,the results suggest that these improved ACOs may find wide applications in the combinatorial optimization filed in the future.

关 键 词：蚁群优化算法 旅行商问题 遗传算法 模拟退火 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O242.23[自动化与计算机技术—控制科学与工程]