New Meta-Heuristic for Combinatorial Optimization Problems:Intersection Based Scaling

查看全文 作　　者：[1]PengZou;[1]ZhiZhou;Ying-[1]YuWan;Guo-[1]LiangChen;[2]JunGu

机构地区：[1]DepartmentofComputerScienceandTechnology,Universityo/ScienceandTechnologyo/China,NationalHighPerformanceComputingCenteratHefei,Hefei230027,P.R.China;[2]DepartmentofComputerScience,HongKongUniversityofScienceandTechnologyHongKongSpecialAdministrativeRegion,P.R.China

出　　处：《Journal of Computer Science & Technology》索引2004年第19卷第6期,共12页

基　　金：国家重点基础研究发展计划(973计划) 摘　　要：Combinatorial optimization problems are found in many application fields such as computer science, engineering and economy. In this paper, a new efficient meta-heuristic, Intersection-Based Scaling (IBS for abbreviation),is proposed and it can be applied to the combinatorial optimization problems. The main idea of IBS is to scale the size of the instance based on the intersection of some local optima, and to simplify the search space by extracting the intersection from the instance, which makes the search more efficient. The combination of IBS with some local search heuristics of different combinatorial optimization problems such as Traveling Salesman Problem (TSP) and Graph Partitioning Problem (GPP) is studied, and comparisons are made with some of the best heuristic algorithms and meta-heuristic algorithms. It is found that it has significantly improved the performance of existing local search heuristics and significantly outperforms the known best algorithms. 关键词：TSP GPP IBS 组合最优化元渐进

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New Meta-Heuristic for Combinatorial Optimization Problems:Intersection Based Scaling

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