A Powerful Genetic Algorithm for Traveling Salesman Problem
This work addresses the TSP, a classic optimization problem, with an incremental improvement in algorithm performance for specific instances.
The paper tackles the Traveling Salesman Problem by proposing a genetic algorithm that uses edge swapping with local search to generate high-quality offspring solutions, achieving competitive results on benchmarks with up to 16,862 cities.
This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP). To construct a powerful GA, I use edge swapping(ES) with a local search procedure to determine good combinations of building blocks of parent solutions for generating even better offspring solutions. Experimental results on well studied TSP benchmarks demonstrate that the proposed GA is competitive in finding very high quality solutions on instances with up to 16,862 cities.