A Weight-Based Multiobjective Genetic Algorithm for Flowshop Scheduling

The study presents a weight-based multiobjective genetic algorithm (WBMOGA). which is modification of a weight-based multiobjective immune genetic algorithm (WBMOIGA) with the following aspects. Firstly, we only use the truncation algorithm with similar individuals (TASI) to eliminate similar individuals in the population and memory and preserve the diversity of the population. Secondly, we modify the local search algorithm by adding the dominated relationship to determine whether the current solution is replaced by a neighborhood solution. Finally, we apply it to the flowshop scheduling problems. Numerical results show WBMOGA can solve the multiobjective optimization problems with multiple variables, and disconnected or nonconvex Pareto-optimal fronts, and demonstrates better performance than the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA) when applied to flowshop scheduling.