Two new selection methods and their effects on the performance of genetic algorithm in solving supply chain and travelling salesman problems Online publication date: Thu, 14-Dec-2023
by Sadegh Eskandari; Marjan Kuchaki Rafsanjani
International Journal of Bio-Inspired Computation (IJBIC), Vol. 22, No. 3, 2023
Abstract: Genetic algorithm (GA) is a well-known evolutionary optimisation method in various operational research areas. Selection is an important operator in GA that provides a trade-off between exploitation and exploration aspects of genetic algorithm. In this paper, two new combinational selection methods called generational sequential mixed selection (GSMS) and generational random mixed selection (GRMS) are presented and compared with six existing selection operators, applied to supply chain network (SCN) design and travelling salesman problems (TSP). The experiments show that the proposed operators achieve better results than existing operators, in every way. Moreover, several state-of-the-art methods are compared with genetic algorithm versions, which adopt the proposed operators. The results on 15 TSPs show that our approach is superior in ten cases. Moreover, the results on ten SCN instances show the superiority of the proposed approach in 50% of the cases.
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