[1]沈继红,王侃.求解旅行商问题的混合粒子群优化算法[J].智能系统学报,2012,7(02):174-182.
 SHEN Jihong,WANG Kan.The light ray particle swarm optimization for solving the traveling salesman problem[J].CAAI Transactions on Intelligent Systems,2012,7(02):174-182.
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求解旅行商问题的混合粒子群优化算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年02期
页码:
174-182
栏目:
出版日期:
2012-04-25

文章信息/Info

Title:
The light ray particle swarm optimization for solving the traveling salesman problem
文章编号:
1673-4785(2012)02-0174-09
作者:
沈继红1王侃2
1.哈尔滨工程大学 理学院,黑龙江 哈尔滨 150001;
2.哈尔滨工程大学 自动化学院,黑龙江 哈尔滨 150001
Author(s):
SHEN Jihong1 WANG Kan2
1.College of Science, Harbin Engineering University, Harbin 150001, China;
2. College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
旅行商问题混沌优化算法费马原理粒子群算法光学寻优算法
Keywords:
travel salesman problem chaos optimization algorithm Ferma’s principle particle swarm optimization light ray optimization
分类号:
TP301.6
文献标志码:
A
摘要:
为高效解决旅行商问题,结合光学寻优算法、混沌优化算法、粒子群优化算法,提出了一种新的混合智能优化算法,应用光学寻优算法的优点,为粒子群中粒子找到了一组最优的初始值,引入交换子、交换序列、混沌序列,提出了适合旅行商问题的光学混沌粒子群算——并严格证明了新算法的稳定性、收敛性.数值实验仿真结果表明,该算法收敛速度快、迭代次数少,能快速找到令人满意的最优解,为解决旅行商问题提供了新的思路.
Abstract:
A new hybrid intelligent optimization was given to solve the traveling salesman problem (TSP) by introducing the thought of an LRO algorithm, chaos optimization algorithm, and particle swarm optimization (PSO). A group of optimal initial values were found by using the features of LRO. Next, by employing the method of discrete chaotic particle swarm optimization and introducing the swap operator, swap sequence, and chaos sequence, an optical chaos PSO adaptive for the TSP problem was proposed. The stability and convergence of the optimization was proved decisively. The numerical simulation results show that this new optimization method has a good convergence rate and less iterative steps, thus allowing a satisfactory solution to be found rapidly. The method provides a new inspiration for solving the TSP problem. 

参考文献/References:

[1]KENNEDY J, EBERHART R. A discrete binary version of the particle swarm algorithm[C]//Proceedings of the World Multiconference on Systemic, Cybernetics and Informatics. Piscataway, USA: IEEE Service Center, 1997: 41044109.
[2]CLERC M. Discrete particle swarm optimization[C]//New Optimization Techniques in Engineering. Berlin:SpingerVerlag, 2004: 204219.
[3]高尚,韩斌,吴小俊,等. 求解旅行商问题的混合粒子群优化算法[J]. 控制与决策, 2004, 19(11): 12861289.
GAO Shang, HAN Bin, WU Xiaojun, et al. Solving traveling salesman problem by hybrid particle swarm optimization algorithm[J]. Control and Decision, 2004, 19(11): 12861289.
[4]HENDLASS T. Preserving diversity in particle swarm optimization[J]. Lecture Notes in Artificial Intelligence, 2003(2718): 155199.
[5]XIE Shenli, TANG Min, DONG Jinxiang. An improved genetic algorithm for TSP problem[J]. Computer Engineering and Application, 2002, 38(8): 5860.
[6]SHEN Jihong, LI Yan. Light ray optimization and its parameter analysis[C]//Proceedings of the 2009 International Joint Conference on Computational Science and Optimization. Kunming, China, 2007: 918922.
[7]沈继红,李焱. 基于正六边形网格的光线寻优算法[C]//中国运筹学会第十届学术交流会论文集. 南京, 中国, 2010: 8994.
SHEN Jihong, LI Yan. Light ray optimization on hexagonal grid[C]//Proceedings of the 10th ORSC. Nanjing, China, 2010: 8994.
[8]SHEN Jihong, LI Jialian. The principle analysis of light ray optimization[C]//2010 Second International Conference on Computational Intelligence and Natural Computing. Wuhan, China, 2010: 154157.
[9]SHI Y, EBERHART R C. A modified particle swarm optimizer[C]//Proceedings of the Congress on Evolutionary Computation. Anchorage, USA, 1998: 6973.
[10]ZHANG Guoping, WANG Zhengou, YUAN Guolin. A chaotic search method for a class of combinatorial optimization problems[J]. Systems Engineering Theory & Practice, 2001, 21(5): 102105.
[11]梁艳春,吴春国.群智能优化算法理论与应用[M].北京: 科学出版社, 2009: 1721.
[12]郑大中.线性系统理论[M].北京: 清华大学出版社, 2009: 213251.
[13]ANDRIES P E.计算智能导论[M].北京: 清华大学出版社, 2010: 111123.

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备注/Memo

备注/Memo:
收稿日期:2011-04-24.
 网络出版日期:2012-03-16.
基金项目:黑龙江省自然科学基金资助项目(F200931).
通信作者:王侃.                E-mail:wangkan198600@163.com.
作者简介:
沈继红,男,1966年生,教授,博士生导师,黑龙江省工业与应用数学学会副理事长,黑龙江省教学名师.主要研究方向为系统优化与建模.完成科研课题12项,获得省级科研和教学奖5项.1996年获霍英东青年教师三等奖,主持的《数学建模》课程获得黑龙江省精品课程,曾获得美国数学建模竞赛一等奖8项,全国大学生数学建模竞赛一等奖1项,全国研究生数学建模竞赛一等奖1项.发表学术论文107篇.
王侃,男,1986年生,博士研究生,主要研究方向为智能优化算法以及复杂系统建模与仿真.
更新日期/Last Update: 2012-07-12