New meta-heuristic for combinatorial optimization problems: intersection based scaling

Authors:
Peng Zou;Zhi Zhou;Ying-Yu Wan;Guo-Liang Chen;Jun Gu
Affiliations:
Department of Computer Science and Technology, University of Science and Technology of China, National High Performance Computing Center at Hefei, Hefei 230027, P.R. China;Department of Computer Science and Technology, University of Science and Technology of China, National High Performance Computing Center at Hefei, Hefei 230027, P.R. China;Department of Computer Science and Technology, University of Science and Technology of China, National High Performance Computing Center at Hefei, Hefei 230027, P.R. China;Department of Computer Science and Technology, University of Science and Technology of China, National High Performance Computing Center at Hefei, Hefei 230027, P.R. China;Univ of Science and Technology of China, National High Performance Computing Center at Hefei, Hefei, China and Hong Kong Univ of Science and Technology, Hong Kong Special Administrative Region, Ch ...
Venue:
Journal of Computer Science and Technology
Year:
2004

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Abstract

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 fbr 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.