Improved particle swarm optimization for flexible job-shop scheduling problem
Particle Swarm Optimization (PSO) is one of the embranchments of swarm intelligence algorithms, which has global performance and efficient in searching optimum results. But the PSO happens at the local optimum when applying in practical problems. This paper presents the improved PSO algorithm which uses evolution mechanism to enhance algorithm's searching ability and adjusts the value of inertia weight to increase speed of convergence according to memory database. The improved PSO algorithm can be applied to solve Flexible Job-shop Scheduling Problem (FJSSP), and its results of simulation indicate that improved PSO algorithm perform better than the genetic algorithm and the heuristic algorithm.