A novel swarm optimiser for flexible flow shop scheduling Online publication date: Sat, 05-Jul-2014
by Manas Ranjan Singh; S.S. Mahapatra; Kaushik Mishra
International Journal of Swarm Intelligence (IJSI), Vol. 1, No. 1, 2013
Abstract: In this work, a particle swarm optimisation (PSO) algorithm with chaotic mutation operator is proposed to solve flexible flow shop scheduling problems. Mutation, a commonly used operator in genetic algorithm, has been introduced so that common problem of trapping of solutions at local minima in PSO can be avoided. Chaotic sequence using logistic mapping is used instead of random numbers to improve the diversity in solution space. The performance of schedules is evaluated in terms of total completion time or makespan (Cmax). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound (LB). The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average percentage deviation is observed as 6.390 whereas GA produces an average percentage deviation of 9.657. Finally, influence of various PSO parameters on solution quality has been investigated.
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