A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

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Abstract
The permutation flow shop scheduling problem (PFSP) which is known to be NP-hard has been widely investigated in recent years. In this paper, an effective hybrid discrete biogeography-based optimization (HDBBO) algorithm is proposed for solving the PFSP with the objective to minimise the makespan. Opposition-based learning method and the NEH heuristic are utilised in the HDBBO to generate an initial population with certain quality and diversity. Moreover, a novel variable local search strategy is presented and incorporated within the biogeography-based optimization framework to improve the exploitation ability. Computational results on two typical benchmark suits and comparisons with some state-of-the-art algorithms are presented to show the effectiveness of the HDBBO scheme.
Funding Information
  • National Natural Science Foundation of China (61503331)
  • Zhejiang Provincial Natural Science Foundation of China (LQ15F030002)
  • Scientific Research Fund of Zhejiang Provincial Education Department (Y201432261)

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