Multiobjective PSO Algorithm with Multi-directional Convergence Strategy to Solve Flow Shop Scheduling Problems

The flow shop scheduling problem (FSP) is a typical combinatorial optimization problem. In order to improve the efficacy in solving the FSP, an multiobjective PSO algorithm with multi-directional convergence strategy (MoPSO-MDCS) is proposed. The multi-directional convergence strategy tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship based fitness function (PDDR-FF). The strategy based on VEGA has a preference for the edge region of the Pareto front, the PDDR-FF strategy has the tendency converging toward the center area of the Pareto front, which preserve both the convergence rate and the distribution performance. The experimental results show that the convergence and distribution performance of MoPSO-MDCS is better than MoPSO, NSGA-II and SPEA2.

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