Integrating the Best 2-Opt Method to Enhance the Genetic Algorithm Execution Time in Solving the Traveler Salesman Problem
The traveling salesman problem (TSP) is one of the classic combinatorial optimization problem NP-complete that requires much time to find the good solution. Indeed, the genetic algorithm is a stochastic optimization algorithm; it is to find an approximate solution of a hard problem. However, genetic algorithm has a great tendency to converge to a local minimum and stay stuck in adverse solutions. To solve this problem, we study in this paper the impact of the integration of a new local optimization heuristic Best 2-opt with the genetic operators on the quality of solution and the runtime of the GA. The hybridization proposed was tested on instances from 29 to 246 cities. The obtained results are very satisfied regarding to the solution qualities and the execution time.
KeywordsGenetic Algorithm Local Search Mutation Operator Travel Salesman Problem Travel Salesman Problem
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