摘要
针对旅行商问题,提出一种结合混沌优化和粒子群算法的新型混沌离散粒子群方法(CIPSO)。新算法根据此类组合优化问题解的固有地形特征,利用混沌运动的遍历性、随机性等特点进行求解,其基本思想是在求解过程中对粒子进行混沌扰动避免陷入局部最优,并引入群体间粒子的交叉作用来提高寻优效率。通过与遗传算法、蚁群算法和模拟退火算法等比较以及不同TSP问题的仿真实验发现,该方法是一种能进行有效优化的新方法。
In this paper,a novel algorithm called CIPSO for short based on particle swarm optimization（PSO） algorithm and chaos optimization algorithm（COA） is presented to solve traveling salesman problem（TSP）.Some new operators are proposed to overcome difficulties of implementing PSO and solve the discrete problem on the basis of the special fitness landscape of TSP.Embedded with chaos theory,the proposed algorithm can enhance particles＇ global searching ability so as to avoid too quick convergence to the local optimal solution,and the introduction of information intercourse can enhance the local searching ability.Compared with SA,GA,ACS,etc.,the new algorithm demonstrates validity and satisfactory effect on several benchmark test problems.
出处
《武汉科技大学学报》
CAS
2011年第2期131-136,共6页
Journal of Wuhan University of Science and Technology
