Abstract
This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time ( C ¯ w ) and weighted mean tardiness ( T ¯ w ) . Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems.
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