A hybrid EDA with ACS for solving permutation flow shop scheduling

This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard’s benchmark data sets demonstrate that the proposed algorithm generated high-quality solutions by comparing with the existing population-based search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.