A Hybrid Genetic-Immune Algorithm with Improved Offsprings and Elitist Antigen for Flow-Shop Scheduling Problems

In this paper, a Hybrid Genetic-Immune algorithm (HGIA) is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-stage to rapidly evolve and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced to hybridize Genetic Algorithm (GA) in the second stage. Therefore, HGIA continues to search optimal solution via co-evolutional process. In the co-evolutionary process, GA and AIS cooperate with each other to search optimal solution by searching different objective functions. One is named fitness in GA section and another one is named antigen which will evoke the withstanding of antibodies. In the process of fighting, the antibodies will evolve till they can resist the antigen. An improved survival strategy of lifespan is also proposed to extend the lifespan of the antibodies as a result the selected antibodies will stay in system longer. The hybrid of GA and AIS can simultaneously contain two objectives. Hence, larger searching space and escaping from local optimal solution will be the superiority for hybridizing GA and AIS. In the research, a set of flow-shop scheduling problems are applied for validating the efficiency. The intensive experimental results show the effectiveness of the proposed approach for Flow-shop problems in Production Scheduling.