Multiple crossover genetic algorithm for the multiobjective traveling salesman problem

Abstract

Abstract Many crossover operators have been proposed and adapted to different combinatorial optimization problems. In particular, many permutation based crossovers are well designed for the traveling salesman problem (TSP) which is among the most-studied combinatorial optimization problems. However, there is no evidence that one crossover operator is superior to another operator. This is specially true for multiobjective optimization. The performance of any genetic algorithm generally varies accordingto the crossover and mutation operators used. We propose to include mutiple crossover and mutation operators with a dynamic selection scheme into a multiobjective genetic algorithm in order to choose the best crossover operator to be used at any given time. The objective is to find a good approximation of the Pareto set. Experimental results on different benchmark data show synergy effects amongdifferen t used crossovers and prove the efficiency of the proposed approach.