Abstract
The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.
Highlights
As a well-known problem in the area of scheduling, permutation flow shop scheduling problem (PFSSP) is defined as the processing sequence of all jobs is the same for all machines, for minimizing the maximum completion time [1]
All parameters in the proposed algorithm are set as the compared algorithms in order to make a fair comparison; only the size of swarm is set differently from the compared algorithms since three subpopulations are employed in the MPSOMA
In order to illustrate the difference between the proposed algorithm and other contrastive algorithms such as PSO based memetic algorithm (PSOMA) and PSOEDA, we use the Wilcoxon matched-pairs signedrank test [28] on the optimal makespan results obtained by 20 independent runs for several test problems
Summary
As a well-known problem in the area of scheduling, PFSSP is defined as the processing sequence of all jobs is the same for all machines, for minimizing the maximum completion time (i.e., makespan) [1]. Some researchers made use of improved heuristic to get the solutions, including simulated annealing algorithm [6], Tabu search method [7], genetic algorithm [8, 9], and particle swarm optimization algorithm [10]. These algorithms get the local optimal value at the most time and have some other limitations. Reference [16] introduced a similar particle swarm optimization algorithm (SPSOA) applied for permutation flow shop scheduling to minimize makespan, which is based on the improvement of the option modes of the global optimal solution and the local optimal solution in PSO.
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