School of Sciences, Xi'an University of Technology, Xi'an 710054, China.
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China.
Comput Intell Neurosci. 2022 Apr 12;2022:1008617. doi: 10.1155/2022/1008617. eCollection 2022.
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.
旅行商问题是一个典型的 NP 难问题和典型的组合优化问题。因此，提出了一种改进的人工协同搜索算法来解决旅行商问题。对于基本的人工协同搜索算法，首先，使用 sigmoid 函数构建比例因子来增强算法的全局搜索能力；其次，在变异阶段，添加差分进化算法的 DE/rand/1 变异策略对当前种群进行二次变异，以提高算法的计算精度和种群的多样性。然后，在算法发展的后期，引入准反转学习策略，进一步提高解的质量。最后，使用改进的人工协同搜索算法解决了几个旅行商问题库（TSPLIB）的实例，并与相关算法进行了比较。结果表明，所提出的算法在解决旅行商问题方面优于对比算法，具有良好的鲁棒性。
