An Estimation of Distribution Algorithm (EDA) variant with QGA for flowshop scheduling problem

In this research article, a hybrid approach is presented which based on well-known meta-heuristics algorithms. This study based on integration of Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm, EDA, (for simplicity we use Q-EDA) for flowshop scheduling, a well-known NP hard Problem, while focusing on the total flow time minimization criterion. A relatively new method has been adopted for the encoding of jobs sequence in flowshop known as angel rotations instead of random keys, so QGA become more efficient. Further, EDA has been integrated to update the population of QGA by making a probability model. This probabilistic model is built and used to generate new candidate solutions which comprised on best individuals, obtained after several repetitions of proposed (Q-EDA) approach. As both heuristics based on probabilistic characteristics, so exhibits excellent learning capability and have minimum chances of being trapped in local optima. The results obtained during this study are presented and compared with contemporary approaches in literature. The current hybrid Q-EDA has implemented on different benchmark problems. The experiments has showed better convergence and results. It is concluded that hybrid Q-EDA algorithm can generally produce better results while implemented for Flowshop Scheduling Problem (FSSP).