Ant Colony Optimization Based on An Improvement Local Search Strategy
نویسنده
چکیده
The traveling salesman problem (TSP) has been an extensively studied problem for a long time and has attracted a considerable amount of research effort. Ant colony system (ACS) algorithm is better method to solve TSP. This paper presents HACS-LS, a hybrid ant colony optimization approach combined with an improvement local search algorithm, applied to traveling salesman problem. The improvement local search algorithm couples heap sort algorithm with 2.5-opt strategy. Four test datasets from TSPLIB, eil76, kroA100, bayg25 and eil51 are chosen to verify the effectiveness of HACS-LS. Experimental results show that HACS-LS performs better than ACS-LS and ACS. The average lengths obtained by HACS-LS are all shorter than those found by ACS-LS and ACS. And then HACS-LS spends less time solving problems than the other algorithms. So the improvement local search algorithm is feasible because of its acceptable time cost.
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