Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

Abstract

─This research work provides a detailed working principle and analysis technique of multioffspring crossover operator. The proposed approach is an extension of the basic partiallymapped crossover (PMX) based upon survival of the fittest theory. It improves the performance of the genetic algorithm (GA) for solving the well-known combinatorial optimization problem, the traveling salesman problem (TSP). This study is based on numerical experiments of the proposed with other traditional crossover operators for eighteen benchmarks TSPLIB instances. The simulation results show a considerable improvement because the proposed operator enhances the opportunity of having better offspring. Moreover, the t-test also establishes the improved significance of the proposed operator. Its preferable results not only confirm the advantages over others, but also show the long run survival of a generation having a number of offspring more than the number of parents with the help of mathematical ecology theory. Index Terms─NP-hard, Traveling salesman problems, Genetic algorithms, Multi-offspring, Crossover operators.