A self-adaptive iterated local search algorithm on the permutation flow shop scheduling problem

Iterated local search (ILS) is a simple, effective and efficient metaheuristic, displaying strong performance on the permutation flow shop scheduling problem minimizing total flow time. Its perturbation method plays an important role in practice. However, in ILS, current methodology does not use an evaluation of the search status to adjust the perturbation strength. In this work, a method is proposed that evaluates the neighborhoods around the local optimum and adjusts the perturbation strength according to this evaluation using a technique derived from simulated-annealing. Basically, if the neighboring solutions are considerably worse than the best solution found so far, indicating that it is hard to escape from the local optimum, then the perturbation strength is likely to increase. A self-adaptive ILS named SAILS is proposed by incorporating this perturbation strategy. Experimental results on benchmark instances show that the proposed perturbation strategy is effective and SAILS performs better than three state of the art algorithms.