摘要
近年来,基于仿生学的随机优化技术成为学术界研究的重点问题之一,并在许多领域得到应用。粒子群优化(PSO)算法和蚂蚁算法ACO(Ant Colong Optimization)是随机全局优化的两个重要方法。PSO算法初始收敛速度较快,但在接近最优解时,收敛速度较慢,而ACO正好相反。结合二者的优势,先利用粒子群算法,再结合蚂蚁算法,以对称旅行商问题为例进行了仿真实现。实验结果表明,先利用PSO算法进行初步求解,在利用蚂蚁算法进行精细求解,可以得到较好的效果。
Recently,the random optimisation technology which is based on bionics has become researching focus in academia,and it has been applied widely in many fields.The Particle Swarm Optimisation（PSO） algorithm and the Ant Colony Optimisation（ACO） are two important random optimisation methods.PSO has a faster convergence speed at beginning but slows down when approaching the optimal solution,while ACO is just in opposite.In this paper we combined the advantages of these two algorithms,used the Particle Swarm Optimisation Algorithm first,and then integrated with the Ant Colony Optimisation,performed this way on symmetry travelling salesman problem for simulative realisation.It is showed by the experimental results,to use the PSO to resolve the problem as the first stage and then to use the Ant Colony Optimisation to further accurately resolve the problem can achieve good effect.
出处
《计算机应用与软件》
CSCD
2010年第1期224-227,共4页
Computer Applications and Software
关键词
粒子群算法
蚂蚁算法
融合
旅行商问题
Particle swarm optimisation algorithm Ant colony optimisation Combination Travelling salesman problem
