摘要
粒子群算法简单、控制参数较少,受到了很多专家学者的关注.但是,粒子群算法也有收敛速度慢、容易陷入局部最优等方面的缺陷.考虑到遗传算法在全局搜索方面很有优势,可以将遗传算法融入到粒子群算法,以弥补粒子群算法的不足.以巡回旅行商为例,分别用matlab仿真标准粒子群算法和粒子群-遗传算法,仿真结果表明粒子群-遗传算法比标准粒子群算法求得的解要更优.
Particle Swarm Optimization is a simple and less-controlling parameters algorithm,which is concerned by lots of experts and scholars.However,particle swarm optimization has some drawbacks,such as slow convergence speed and easily falling into local optimum.Considering of the advantages of genetic algorithm in the global search,genetic algorithm can be integrated into particle swarm optimization to compensate for the disadvantage of particle swarm algorithm.Taking traveling salesman problem for example,the simulation results show that the result of particle swarm optimization-genetic algorithm is better than that of standard particle swarm algorithm.
作者
李雅琼
LI Ya-qiong(Fuyang Vocational And Technical College, Fuyang 236000, Anhui, Chin)
出处
《兰州文理学院学报（自然科学版）》
2017年第1期55-60,共6页
Journal of Lanzhou University of Arts and Science(Natural Sciences)
基金
阜阳职业技术学院校级科研项目(2014KYXM07)
安徽省质量工程机电一体化技术教学团队(2014jxtd058)
安徽省高职教育创新发展行动计划机电一体化骨干专业建设(XM01)
关键词
巡回旅行商
粒子群-遗传算法
MATLAB仿真
traveling salesman problem
particle swarm optimization-genetic algorithm
matlab simulation
