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Abstract
This paper presents a dynamic multi-swarm particle swarm optimizer (DMS-PSO) for solving the blocking flow shop scheduling problem with the objective to minimize makespan. To maintain good global search ability, small swarms and a regrouping schedule were used in the presented DMS-PSO. Each small swarm performed searching according to its own historical information, whereas the regrouping schedule was employed to exchange information among them. A specially designed local search phase was added into the algorithm to improve its local search ability. The experiments based on the well-known benchmarks were conducted. The computational results and comparisons indicated that the proposed DMS-PSO had a better performance on the blocking flow shop scheduling problems than some other compared algorithms in the literature.
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