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標題:Imperial competitive algorithm with policy learning for the traveling salesman problem
學年104
學期1
出版(發表)日期2015/10/01
作品名稱Imperial competitive algorithm with policy learning for the traveling salesman problem
作品名稱(其他語言)
著者Chen, M. H.; S. H. Chen; P. C. Chang
單位
出版者
著錄名稱、卷期、頁數Soft Computing 21(7), pp 1863–1875
摘要The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring; the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman’s tour with more effective performance levels than other known methods.
關鍵字Traveling salesman problem;Imperial competitive algorithm;Combinatorial optimization problems;Artificial chromosomes;Genetic algorithm
語言英文
ISSN1432-7643
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別德國
公開徵稿
出版型式,電子版,紙本
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