Abstract
The traveling salesman problem is a typical NP hard problem and a typical combinatorial optimization problem. Therefore, an improved artificial cooperative search algorithm is proposed to solve the traveling salesman problem. For the basic artificial collaborative search algorithm, firstly, the sigmoid function is used to construct the scale factor to enhance the global search ability of the algorithm; secondly, in the mutation stage, the DE/rand/1 mutation strategy of differential evolution algorithm is added to carry out secondary mutation to the current population, so as to improve the calculation accuracy of the algorithm and the diversity of the population. Then, in the later stage of the algorithm development, the quasi-reverse learning strategy is introduced to further improve the quality of the solution. Finally, several examples of traveling salesman problem library (TSPLIB) are solved using the improved artificial cooperative search algorithm and compared with the related algorithms. The results show that the proposed algorithm is better than the comparison algorithm in solving the travel salesman problem and has good robustness.
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