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基于自适应转移概率的蚁群优化算法 被引量：8

Ant Colony Optimization Algorithm Based on Adaptive Transition Probability
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摘要 为避免蚁群优化算法容易早熟的缺点,在转移概率公式中引入一个新的自适应因子。随着迭代次数的增加,该因子有利于蚂蚁探索有较弱信息素浓度的边而避免一些边上信息素的过度积累。该特点使蚂蚁在迭代后期仍能以较高概率搜索到更好的解从而避免早熟。仿真实验结果表明,该算法对解决旅行商问题具有更优的全局搜索能力。 A new factor in transition rule is employed to overcome the premature behavior in Ant Colony Optimization（ACO）.The factor can help the ants to obtain a better result by exploring the arc with low pheromone trail accumulated so far as time elapses.Besides,it can avoid the over-concentration of pheromone trail to enlarge the searching range.Simulation results show that the Improved Ant Colony System（IACS） has better performance in solving Traveling Salesman Problem（TSP） and more outstanding global optimization properties.
作者 何雪海 胡小兵 赵吉东 王志
机构地区 重庆大学数理学院
出处 《计算机工程》 CAS CSCD 北大核心 2010年第23期165-167,共3页 Computer Engineering
基金 重庆市自然科学基金资助项目"群集智能理论 模型及其仿真研究"(CSPC 2005BB2197) 重庆大学高层次人才科研启动基金资助项目(020800110420) 重庆大学数理学院青年科研启动基金资助项目
关键词 蚁群优化 自适应转移概率 旅行商问题 Ant Colony Optimization（ACO） adaptive transition probability Traveling Salesman Problem（TSP）
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献8

  • 1Dorigo M, Maniezzo V, Colorni A. The Ant System: Optimization by a Colony of Cooperating Agents [J].IEEE Trans. on Systems, Man, and Cybernetics, 1996, 26(1): 29- 41.
  • 2Stutzle T. MAX-MIN Ant System [J]. Future Generation Computer Systems, 2000, 16(8): 889-914.
  • 3Dorigo M, Gambardella L M. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem[J].IEEE Trans. on Evolutionary Computation, 1997, 1(1): 53-66.
  • 4Dorigo M, Stutzle T. Ant Colony Optimization[M]. Cambrige, UK: MIT Press, 2004.
  • 5Ellabib I, Calamai P, Basir O. Exchange Strategies for Multiple Ant Colony System[J]. Information Sciences, 2007, 177 (5): 1248-1264.
  • 6Wu Zhilu, Zhao Nan, Ren Guanghui, et al. Population Declining Ant Colony Optimization Algorithm and Its Applications[J ]. Expert Systems with Applications, 2009, 36(3) : 6276-6281.
  • 7TSPLIB-A Traveling Salesman Problem Library [EB/OL]. (2009- 10-23). http://www, iwr. uni-heidelberg, de/groups/ comopt/software/TSPLIB95/tsp/.
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二级参考文献12

  • 1Dorigo M, Stutzle T. Ant colony optimization[M]. Cambridge: MIT Press/Bradford Books, 2004.
  • 2Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents [J]. IEEE Trans on Systems, Man, and Cybernetics-Part B, 1996, 26(1): 29-41.
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  • 7Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the traveling salesman problem[J]. IEEE Trans on Evolutionary Computation, 1997, 1(1):53-66.
  • 8Thomas Stutzle, Holger H Hoos. MAX-MIN ant system [J]. Future Generation Computer Systems, 2000, 16(8):889-914.
  • 9Shu-Chuan Chu, John F Roddick, Jeng-Shyang Pan. Ant colony system with communication strategies[J]. Information Sciences, 2004, 167(7):63-76.
  • 10Paulo Henrique Siqueira, Maria Teresinha Arns Steiner, Sergio Seheer. A new approach to solve the traveling salesman problem [ J ]. Neuroeomputing, 2006, 70(4): 1013-1021.

共引文献39

同被引文献77

  • 1梁凯,毛剑琳.基于改进蚁群算法的移动机器人动态路径规划[J].电子测量技术,2020(7):56-60. 被引量：10
  • 2李元诚,焦润海,李波.一种基于支持向量机的小波图像压缩方法[J].北京航空航天大学学报,2006,32(5):598-602. 被引量：9
  • 3马溪骏,潘若愚,杨善林.基于信息素递减的蚁群算法[J].系统仿真学报,2006,18(11):3297-3300. 被引量：18
  • 4DORIGOM,STUTZLET.蚁群优化[M].张军,胡晓敏,罗旭耀,译.北京:清华大学出版社,2007:216-246.
  • 5DORIGO M, MANIEZZO V, COLORNI A. Ant system: optimization by a colony of cooperating agents [ J]. IEEE Trans on Systems, Man, and Cybernetics, 1996,26 ( 1 ) :29-41.
  • 6Dorigo M,Stutzle T. Ant Colony Optimization[M]. UK:The M IT Press, 2009.
  • 7Xiong P C,Fan Y S,Zhou M C. QoS-aware web service configuration[J]. IEEE Transactions on Systems ,Man and Cybernetics ,2008,38(4) : 888-895.
  • 8Assuncao M,Costanzo A,Buyya R. Evaluating the cost benefit of using cloud computing to extend the capacity of clusters [C ]//Proc of the 18th ACM international symposium on High performance distributed computing.New York :ACM,2009: 141-150.
  • 9Dorigo M, Mamezzo V, Colomi A. Ant system: Optimization by a colony cooperating Agents[ J]. IEEE Trans. on Systems, 1996, 26(1) :29 -41.
  • 10Stutzle T, Hoos H H. MAX - MIN ant system [ J ]. Future Generation Computer Systems ,2000,16 ( 8 ) :889 - 914.

引证文献8

二级引证文献65

计算机工程
2010年 第23期

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