Abstract
Traveling salesman problem (TSP) is one of the most famous NP-hard problems, which has wide application background. Ant colony optimization (ACO) is a nature-inspired algorithm and taken as one of the high performance computing methods for TSP. Classical ACO algorithm like ant colony system (ACS) cannot solve TSP very well. The present paper proposes an ACO algorithm with multi-direction searching capacity to improve the performance in solving TSP. Three weight parameter settings are designed to form a new transition rule, which has multi-direction searching functions in selecting the edges of the TSP tour. The experimental results of solving different kinds of TSP problems indicate the proposed algorithm performs better than the famous ACO algorithm ACS.
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