Hybrid genetic algorithm for optimization problems with permutation property

Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was developed by integrating both the evolutional and the neighborhood search for permutation optimization. Experimental results of a production scheduling problem indicate that the hybrid genetic algorithm outperforms the other methods, in particular for larger problems. Numerical evidence also shows that different input data from the initial, transient and steady states influence computation efficiency in different ways. Therefore, their properties have been investigated to facilitate the measure of the performance and the estimation of the accuracy.