Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2010.699.703HernaneS.HernaneY.BenyettouM.82010108We aim in this study to measure the performance of a distributed algorithm of Particle Swarm Optimization. The PSO is a bio-inspired algorithm founded on the cooperative behaviour of agents and is known as a tool to address difficult problems in numerous and divers fields. Like evolutionary algorithms, PSO offer practical approach to solve complex problems of realistic scale and gave results at least satisfactory. In addition, the performance of production systems is related to the scheduling of work on the one hand and to the assignment of this work of the various machines of the system on the other hand. The problem is noted Np-complete. Nevertheless, it remains that this algorithms require large computational demand in terms of CPU time and memory. Also, it is possible to improve solutions quality in various manners. In this research, we study the adequacy of a parallel distributed PSO algorithm for a scheduling problem in hybrid flow-shop (FSH) systems. We use a fault-tolerant environment by exploiting the computing power of a high-performance cluster with homogeneous processors. For this purpose, we study a parallel distributed model of PSO algorithm on a high-performance cluster with homogeneous processors. Experimental tests are compared with those obtained by the parallel genetic algorithms with migration.]]>Hao, C., N.S. Flann and D.W. Waston,1998Kennedy, J. and R. Eberhart,1995Li-Ping, Z., Y. Huan-Jun and H. Shang-Xu,2005Pruyne, J. and M. Livny,1995Requilé, G.,1995Hu, X., R.C. Eberhart and Y. Shi,2003Parsopoulos, K.E., D.E. Tasoulis and M.N. Vrahatis,2004