Permutation rules and genetic algorithm to solve the traveling salesman problem

Abstract

In this paper, a new approach including permutation rules and a genetic algorithm is proposed to solve the symmetric travelling salesman problem. This problem is known to be NP-Hard. In order to increase the efficiency of the genetic algorithm, the initial population of feasible solutions is carefully generated. In addition to that, dynamic crossover and mutation rates were developed. The proposed method was successfully tested using large numbers of different-sized benchmarks. The computational results proved that the proposed solution approach outperforms many existing methods. In addition, for many problem instances the proposed algorithm is able to generate solutions with same value as the best known solutions.