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A Probabilistic Modeling Clonal Selection Algorithm and Its Application to Traveling Salesman Problems


Huiqin Chen, Shangce Gao, Sheng Li, Zheng Tang


Vol. 10  No. 6  pp. 57-62


In this paper, we propose a probabilistic modeling clonal selection algorithm which combines the clonal selection algorithm (CSA) and the probabilistic modeling (PM) for traveling salesman problem (TSP). The clonal selection algorithm is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus, can initialize antibodies and maintain the population diversity. Furthermore, the PM phase attempts to reduce the computational complexity, generate new solution offspring for the CSA phase. Simulations on traveling salesman problems show that the proposed algorithm has better performance when compared with other traditional algorithms.


Clonal selection algorithm, probabilistic modeling, receptor gene, traveling salesman problems