摘要
蚁群算法是模仿蚂蚁觅食行为的一种新的仿生学智能优化算法。针对其收敛速度慢和易陷入局部最优的不足,将细菌觅食算法和蚁群算法相结合,提出一种细菌觅食-蚁群算法。在蚁群算法迭代过程中,引入细菌觅食算法的复制操作,以加快算法的收敛速度;引入细菌觅食算法的趋向操作,以增强算法的全局搜索能力。通过经典的旅行商问题和函数优化问题测试表明,细菌觅食-蚁群算法在寻优能力、可靠性、收敛效率和稳定性方面均优于基本蚁群算法及两种改进蚁群算法。
Ant colony optimization is a new bionic intelligent optimization algorithm to mimics the foraging behavior of ants.Aiming at the problems of local optimum and slow convergence speed of ant colony optimization,we propose a bacteria foraging ant colony optimization algorithm by combining the bacterial foraging algorithm with the ant colony algorithm.In the iterative process of ant colony optimization,a reproduction process is introduced to the ant colony optimization to accelerate the convergence speed.A chemo taxis process is introduced to enhance the global searching ability.Simulation experiments on the classic traveling salesman problem and function optimization problem show that,compared with the traditional ant colony optimization and two improved ant colony optimization algorithms,the proposed algorithm is more effective in optimization capability,reliability,convergence efficiency and stability.
作者
张立毅
肖超
费腾
ZHANG Li-yi;XIAO Chao;FEI Teng(School of Information Engineering,Tianjin University of Commerce,Tianjin 300134;School of Economics,Tianjin University of Commerce,Tianjin 300134,China)
出处
《计算机工程与科学》
CSCD
北大核心
2018年第10期1882-1889,共8页
Computer Engineering & Science
基金
国家自然科学基金(61401307)
中国博士后科学基金(2014M561184)
天津市应用基础与前沿技术研究计划项目(天津市自然科学基金)(16JCYBJC28800)
关键词
蚁群算法
细菌觅食算法
旅行商问题
ant colony algorithm
bacteria foraging algorithm
traveling salesman problem
