A hybrid bio-geography based optimization for permutation flow shop scheduling

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a biogeography based optimization (BBO) based on memetic algorithm, named HBBO is proposed for PFSSP. Firstly, to make BBO suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in BBO to the discrete job permutation. Secondly, the NEH heuristic was combined with the random initialization to initialize the population with certain quality and diversity. Thirdly, a fast local search is used for enhancing the individuals with a certain probability. Fourthly, the pair wise based local search is used to enhance the global optimal solution and help the algorithm to escape from local minimum. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, showing that our algorithm is both effective and efficient.