Improved Shuffled Frog Leaping Algorithm for Multi-objection Flexible Job-Shop Scheduling Problem
This paper proposes an improved shuffled frog leaping algorithm (SFLA-PSO) to solve multi-objective flexible job-shop problem. Pareto optimization strategy is used to balance three objectives including minimum the maximum completion time, maximum workload of all machines and the total processing time of all job. In the new algorithm, the particle swarm flight strategy is innovatively embedded into the local update operator of the SFLA-PSO. A dynamic crowding density sorting method is used to update external elite archive which holds the non-dominated population. To further improve the quality of the solution and the diversity of the population, a new local optimization strategy is developed associated with a neighborhood search strategy for achieving a high development ability of the new algorithm. The test results of benchmark instances illustrate the effectiveness of the new algorithm.
KeywordsFlexible job-shop scheduling Multi-objective optimization Pareto optimum solution Shuffled frog leaping algorithm Particle swarm algorithm
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