A Comparative Study of Genetic Algorithm and Particle Swarm Optimization in Context of Plant Layout Optimization

Facility layout problem, which involves planning, designing and optimization of physical arrangement of resources, has significant impact on manufacturing systems. A good placement of facilities contributes to the overall efficiency of operations and reduces total operating expenses. Because of its importance, the facility layout problem has attracted attention of many researchers. Due to the combinatorial nature of this problem, during the last decades, several metaheuristics have been applied to obtain efficient solutions. These approaches have also provided a new perspective on this area. Nowadays many researches are going on using hybrid algorithms and artificial intelligence techniques to optimize layout problems in which multiobjective functions are taken into considerations. Genetic Algorithms (GA) and Particle swarm optimization (PSO) techniques are very adaptively used to solve multiobjective complex problems. The two approaches find a solution to a given objective function employing different procedures and computational techniques; as a result their performance can be evaluated and compared. This paper attempts to examine the claim that PSO has the same effectiveness as the GA but with significantly better computational efficiency (less function evaluations) by implementing statistical analysis and formal hypothesis testing. This paper proposes a new technique that depends basically on forcing PSO to start from initial solutions that guarantee feasible domain obtained using GA. Thus, PSO will be able to define the global optimal solution avoiding the long processing time associated with GA. The major objective of this paper is to compare the computational effectiveness and efficiency of the GA and PSO using a formal hypothesis testing approach