Hybridization of soft computing techniques for flow shop scheduling problem

Abstract

--Since the last two decades, metaheuristic algorithms were developed in almost every aspect of various problems, where computational intelligence is used. In the field of computer science and operation research, PSO is used which is an optimization algorithm inspired by the social behaviour of birds flocking and fishes shoaling with its group. The original PSO was used to solve continuous optimization problems. Crossover and mutation of the particle is modified due to the discrete solution spaces of scheduling the optimization problems. Cuckoo search idealized such as breeding behaviour can be applied for optimization problems and it is successfully applied to various paths mostly continuous optimization problems. Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The job scheduling problem is used for assigning the Flexible job shops in a way that will optimize the overall performance of the application, while assuring the correctness of the result. PSO and CS algorithm is proposed in this paper, for solving the Flexible job shop scheduling problem with an intention to decrease the maximum completion time. This paper has modifications to the PSO algorithm, which is based on Genetic Algorithm (GA) of crossover and mutation operators. Such modifications applied to the creation of new candidate solutions to improve the performance of the algorithm. Thus the use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the Flexible job shop scheduling problem. This paper shows that the proposed algorithm exhibits more outstanding performance than PSO-GA.