Hybrid particle swarm algorithm for job shop scheduling problems

Particle swarm optimization (PSO) algorithm is a kind of random optimization algorithm based on swarm intelligence. Swarm intelligence of PSO is produced by cooperation and competition between particles, which is used for guiding optimization search. PSO has been studied widely in many applications due to its good global searching ability. Currently PSO has been widely used in function optimization, neural network training, pattern classification, system control and other applications. The research on PSO in recent years indicates that PSO has fast convergence speed and good quality in solutions and fine robustness on optimization in multidimensional space functions or in dynamic objectives, which is suitable for project applications. In this chapter, we firstly introduce searching mechanism and algorithm processes of PSO. Then, some important problems are solved when PSO is used for job shop scheduling problems (JSSP), such as hybrid algorithms between particle swarm and other algorithms (HPSO), its deadlock issues, and the proof of PSO and HPSO convergence. This chapter can provide guides effectively for readers who apply particle swarm optimization algorithm.