An Improved Immune-Genetic Algorithm for the Traveling Salesman Problem

Abstract

An improved immune-genetic algorithm is applied to solve the traveling salesman problem (TSP) in this paper. A new selection strategy is incorporated into the conventional genetic algorithm to improve the performance of genetic algorithm. The selection strategy includes three computational procedures: evaluating the diversity of genes, calculating the percentage of genes, and computing the selection probability of genes. Computer numerical experiments on two case studies (21-city and 56-city TSPs) are performed to validate the effectiveness of the improved immune-genetic algorithm. The results show that by incorporating inoculating genes into conventional procedures of genetic algorithm, the number of evolutional iterations to reach an optimal solution can be significantly reduced.