On the Influence of Parameters in Particle Swarm Optimisation Algorithm for Job Shop Scheduling

Particle Swarm Optimization (PSO) is one of the latest nature inspired meta-heuristic algorithms based on the metaphor of social interaction and communication such as bird flocking and fish schooling. PSO is a population based algorithm for finding optimal regions of complex search spaces through interaction of individuals in the population. In PSO, a set of randomly generated solutions (initial swarm) navigate in the design space towards the optimal solution over a number of iterations (moves) based on large amount of information about the design space that is assimilated and shared by the members of the swarm. The solution of PSO depend on the parameter setting such as swarm size, number of generations, inertia factor, self confidence factor and swarm confidence factor. This work describes the study on the influence of parameter settings in PSO algorithm in solving the Static Job Shop Scheduling Problem, which is a NP-hard combinatorial problem. Performance of the algorithm is tested on benchmark problems. The influence of each PSO parameter on the performance of algorithm is studied in detail. Key-Words:Combinatorial optimization, nature inspired meta-heuristic, PSO, Scheduling, Job shop, parameter tuning