离散社会群体优化算法求解旅行商问题

A Discrete Social Group Optimization for TSP

社会群体优化算法(SGO)已经应用在求解连续域问题上,而在离散优化问题上的应用还相对较少。本文首先介绍了旅行商问题(TSP)和社会群体优化算法的原理,然后根据旅行商问题和离散量的特点对SGO算法的运算规则进行了重新定义。在SGO算法的提高和获得阶段分别引入交叉、变异操作,有效地增加了种群的多样性,减小了算法陷入局部最优的可能,从而提高了算法的全局收敛速度。在标准TSP测试数据下进行了相关实验,实验结果表明利用社会群体优化算法求解旅行商问题能取得较好的结果。

The social group optimization algorithm（ SGO） has been applied to solve the continuous domain problem,but the application on the discrete optimization problem is relatively few. In this paper,the principle of traveling salesman problem（ TSP） and social group optimization algorithm is introduced,and the operation rules of the SGO algorithm are redefined according to the traveling salesman problem and the characteristics of the discrete quantity. In the improvement and acquisition stage of SGO algorithm,crossover and mutation operations are introduced respectively,which effectively increases the diversity of population and reduces the possibility of algorithm falling into local optimum,so that the global convergence speed of the algorithm is improved. The experimental results are carried out under the standard traveling salesman problem test data,the results show that the social group optimization algorithm can achieve better results in solving the traveling salesman problem.