An Improved Immune-Genetic Algorithm for the Traveling Salesman Problem

Authors:
Jingui Lu;Ning Fang;Dinghong Shao;Congyan Liu
Affiliations:
Nanjing University of Technology, China;Utah State University, USA;Nanjing University of Technology, China;Nanjing University of Technology, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Year:
2007

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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.