Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm
Автор: Ehtasham-ul-Haq, Abid Hussain, Ishfaq Ahmad
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 12 vol.11, 2019 года.
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This research work provides a detailed working principle and analysis technique of multi- offspring crossover operator. The proposed approach is an extension of the basic partially- mapped 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.
NP-hard, Traveling salesman problems, Genetic algorithms, Multi-offspring, Crossover operators
Короткий адрес: https://sciup.org/15017116
IDR: 15017116 | DOI: 10.5815/ijisa.2019.12.05
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