The optimization of job shop scheduling problem based on Artificial Fish Swarm Algorithm with tabu search strategy

Abstract

The job shop scheduling problem (JSSP) is a sort of famous combination optimization problems which is difficult to solve using the conventional optimization algorithm. Artificial Fish Swarm Algorithm (AFSA) proves to be powerful in solving some optimization problems and the AFSA has the advantages of not strict to parameter setting, strong robustness, fast convergence and so on. In this paper, the tabu search strategy is added into the AFSA to avoid artificial fish (AF) being trapped in the local optimum and speed up the convergence. Some well known benchmark problems in JSSP are used to evaluate the performance of the AFSA with tabu search strategy. The simulation result shows that the performance of AFSA with tabu search strategy in solving JSSP is satisfactory.