In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems. ©Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Liu, L., Wang, D., & Yang, S. (2009). An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman Problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5484 LNCS, pp. 725–734). https://doi.org/10.1007/978-3-642-01129-0_82
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