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Ant colony algorithm (ACA) is a very effective way for traveling salesman problem (TSP) due to the positive feedback and the distributed parallel computing mechanism. But it has many drawbacks, for example, long search time, precocity and stagnation. In order to speed up convergence rate and ensure the global search ability, a novel ant colony algorithm was proposed. After every round of search, it uses mutation operations of genetic algorithm to optimize tours and quicken the convergence by comparing path length before and after variation. Experiments show that the improved ant colony algorithm can overcome prematurity and obtain the higher solution accuracy than ACA.

A Novel Ant Colony Algorithm with Mutation Operations of Genetic Algorithm for TSP