An experimental study of a hybrid genetic algorithm for the maximum traveling salesman problem

Purpose: In this paper, we consider the maximum traveling salesman problem, a variation of the usual traveling salesman problem, in which the objective is to maximize the cost of a tour of the salesman. The main purpose of this paper is to develop a hybrid genetic algorithm (GA) for obtaining a heuristically optimal solution to the problem. Methods: First, a simple GA and then a hybrid GA have been proposed to solve the problem. As crossover operator plays a vital role in GAs, we modify the sequential constructive crossover operator for our simple GA to solve the problem. To improve the quality of the solution obtained by the crossover operator, restricted 2-opt search is applied. Then a hybrid GA is developed by incorporating a new local search algorithm to the simple GA in order to obtain a heuristic solution to the problem. Results: We compare the efficiency of our hybrid GA against an existing heuristic algorithm for symmetric traveling salesman problem library (TSPLIB) instances. Finally, we present solutions to the problem for asymmetric TSPLIB instances. Since, to the best of our knowledge, no literature presents solution for asymmetric instances, hence, we could not carry out any comparative study to show the efficiency of our hybrid GA for the asymmetric instances. Conclusions: The comparative study shows the effectiveness of our hybrid GA.