An Improved Weight-Based Multiobjective Genetic Algorithm and Its Application to Parallel Machine Scheduling

In this study, a weight-based multiobjective genetic algorithm (WBMOGA) is improved. Different from WBMOGA, the modified algorithm presents a novel selection approach based on the truncation algorithm with similar individuals (TASI), and is applied to the parallel machine scheduling in the textile manufacturing industry. Simulation results show that the modified WBMOGA can better solve the parallel machine scheduling problems, and find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA).