An Approach for Solving Traveling Salesman Problem using Hybrid Ant Colony Optimization

Traveling salesman problem (TSP) is one ofthe most famous combinatorial optimization(CO) problems, which has wide applicationbackground. Ant Colony Optimization (ACO) isa heuristic algorithm which has been proven asuccessful technique and applied to a number ofcombinatorial optimization problems and takenas one of the high performance computingmethods for TSP. ACO has very good searchcapability for optimization problems, but it stillhas some drawbacks for solving TSP. Thesedrawbacks will be more obvious when theproblem size increases. The present paperproposes an ACO algorithm with nearestneighbor (NN) heuristic approach andinformation entropy which is conducted on theconfiguration strategy for the adjustableparameters to improve the efficiency of ACO insolving TSP. The performance of ACO alsodepends on the appropriate setting ofparameters. Then, ACO for TSP has beenimproved by incorporating local optimizationheuristic. Algorithms are tested on benchmarkproblems from TSPLIB and test results arepresented. From our experiments, the proposedalgorithm has superior search performance overtraditional ACO algorithms do