Immune Optimization Algorithm for Traveling Salesman Problem Based on Clustering Analysis and Self-Circulation

In order to improve the population diversity during immune optimization of high-dimensional TSP problem, avoid falling into local optimal solutions, and then improve the algorithm's global optimization capability and optimization efficiency, Immune Optimization Algorithm for TSP Based on Cluster Analysis and Self-Circulation, (IOATCS) is proposed. Inspired by cluster analysis, TSP population data is classified and evolved according to the relevance of the problem to improve the efficiency of population optimization; a self-circulation strategy is designed to obtain new offspring through the self-circulation and crossover of the parent body, and then inherit the gene fragments of excellent individuals to the offspring, which improves the global search ability of the algorithm. The test results of six groups of high-dimensional TSP problems show that compared with the basic genetic algorithm, immune genetic algorithm and ant colony algorithm, the planning performance of the algorithm in this paper is the best, and the average relative error and relative error standard deviation of the algorithm are reduced by 10.21% and 4.54% respectively, and the average convergence algebra and convergence algebra standard deviation are reduced by 31.2% and 38.7% respectively, which verifies the strong optimization ability, high optimization efficiency and good stability of the proposed algorithm.