Clonal Selection of Artificial Immune System for Solving the Capacitated Vehicle Routing Problem

Vehicle routing is one of the crucial logistic activities for delivering goods or services. This paper demonstrates the application of the clonal selection of Artificial Immune System (AIS), Generalized Evolutionary Walk Algorithm (GEWA) and Genetic Algorithm (GA) for solving capacitated vehicle routing problem (CVRP). The optimal parameter settings of the proposed algorithms were investigated using statistical design and analysis of experiment. Sequentially, the performances of the proposed algorithms were compared using twenty benchmarking CVRP instant datasets. The results showed that the parameters of each algorithm were statistically significant with a 95% confidence interval. It was found that the best-so-far solutions obtained from the AIS were up to 71.4% more efficient than those produced by the GEWA and GA for all problem sizes but the AIS required longer computational time.