A novel completely mapped crossover operator for genetic algorithm to facilitate the traveling salesman problem

Abstract

As an intelligent search technique, genetic algorithm (GA) is a key member of optimization research methods. Motivated by the ability of GA in resolving complex and nondeterministic polynomial problems, this research proposes a new operator, namely, completely mapped crossover operator to facilitates the search of optimal solutions. Under a very general setting of the state-of-the-art parameters, the performance of newly proposed method is explored with respect to five vibrant and most commonly used techniques existent in the literature. Furthermore, the generality of the applicability of contemporary techniques is maintained by considering eighteen benchmarks from the library of traveling salesman problem, possessing different levels of complexity. Based on diverse performance assessment criteria, we observe that propose method outperforms the contemporary alternatives.