Comparison of evolution algorithms coupled with a* search for solving facility layout problem
Résumé
Optimization metaheuristics solve the complex facility layout problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods vs in solving a constrained facility layout problem. We consider their speed in relation to the performance of the obtained solutions, after their convergence, in terms of the total distance traveled by the parts in the workshop. The two chosen metaheuristics have been successfully applied in many search problems: the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). In order to determine the shortest path between equipment in a given irregular area (with walls, obstacles, or fixed installations), the A* algorithm was combined with them. The comparison therefore concerns the two methods and . GA and PSO algorithms generate potential solutions for which the shortest path for any couple of machines is determined through the A* search algorithm by bypassing the obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in [1]. The numerical experiments demonstrate the feasibility and effectiveness of both approaches. The results show that GA provides better solutions in terms of fitness values while PSO is faster.