Knowledge Driven Improved Meme Algorithm for Solving Multi-objective Distributed Green Flexible Job Shop Scheduling Problem

In order to effectively solve the multi-objective distributed flexible job shop scheduling problem aimed at minimizing maximum completion time and total energy consumption, this study proposes a hybrid multi-objective improved meme algorithm, Designed integrated process priority crossover and universal crossover search operators to enhance the comprehensiveness of the search. A variable neighborhood search operator based on disjunction graph and critical block is introduced to improve the convergence performance of the algorithm, Aiming at the goal of reducing total energy consumption, a gradual energy-saving strategy from semi-active decoding to active decoding and then to fully active decoding has been proposed. In order to verify the effectiveness of the proposed algorithm, separation experiments and comparative experiments were designed. By comparing with algorithms proposed by other researchers recently, the experimental results show that the proposed algorithm exhibits superior performance in distributed green flexible job shop scheduling problems, significantly outperforming other compared algorithms.