Improved NSGA-II algorithm for multi-objective scheduling problem in hybrid flow shop

In this paper, multi-objective optimization for hybrid flow shop scheduling problem is investigated. The delivery time penalty and the load imbalance penalty are taken as the evaluation metrics. We describe the optimization framework for this hybrid flow shop problem and design an improved NSGA-II algorithm for solution searching. Specifically, a multi-objective dynamic adaptive differential evolution algorithm (MODADE) is proposed to enhance the searching efficiency of the basic differential evolution operations. MODADE calculates the similarity between different individuals based on their Hamming distance, and dynamically generates the high-similarity individuals for the population. We further improve the MODADE algorithm by integrating the AP clustering mechanism. We compare the proposed algorithm and compare it with the state-of-the-art solutions. The numerical result shows that the proposed MODADE algorithm outperforms others in terms of the algorithm convergence, the number, and distribution of Pareto solutions