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基于改进樽海鞘群算法的多目标柔性作业车间调度问题研究

Research on Multi-objective Flexible Job Shop Scheduling Problem Based on Improved Salp Swarm Algorithm

  • 摘要: 针对多目标柔性作业车间调度问题,构建了以最小化总能耗、最小化生产成本及最小化惩罚值为优化目标的数学模型,并设计改进的多目标樽海鞘群算法(IMSSA)进行求解。改进算法主要由樽海鞘领导者和樽海鞘追随者两部分构成,其中,领导者位置更新结合正余弦算法来实现,追随者位置更新基于线性微分递减的惯性权重方法来完成。此外,引入食物源存储库用于保留非支配解。最后通过对比实验证明了所提策略及改进算法的有效性。

     

    Abstract: For the multi-objective flexible job shop scheduling problem, a mathematical model with the optimization objectives of minimizing total energy consumption, minimizing production cost, and minimizing penalty value is constructed, and an improved Multi-objective Salp Swarm Algorithm (IMSSA) is designed to solve the problem. The improved algorithm mainly consists of two parts: leaders and followers, where the leader’s position update is implemented in combination with the sine cosine algorithm and the follower’s position update is done based on the linear differential decreasing inertia weight method. In addition, the food source repository is introduced to retain the non-dominated solutions. Finally, the comparative experiments proved the effectiveness of the proposed strategy and the improved algorithm. 

     

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