An HSGA hybrid algorithm to solve job shop scheduling problem

A hybrid metaheuristic algorithm (HSGA) based on genetic algorithm and harmony search algorithm is proposed to solve job shop scheduling problem. Taking genetic algorithm as framework, introducing harmony search algorithm as mutation operator, the HSGA algorithm improves the algorithm efficiency by using the global optimization characteristic of harmony search. Taking several job shop benchmark problems as experiment data, computational experiments are conducted to verify the performance of HSGA. Throughout the experiment results, it is proved that HSGA can effectively improve the lack of traditional genetic algorithms, and get more satisfied effectiveness and efficiency comparing with other classical metaheuristic algorithms.