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针对缓冲区有限的多目标流水车间调度问题,提出一种基于Pareto最优的广义多目标萤火虫算法.通过引入交换子和交换序将基本萤火虫算法离散化,并将算法拓展为全局搜索过程和局部搜索过程.进化初期采用全局搜索将种群推向较优区域,进化中后期采用捕食搜索策略使算法主体在全局搜索和局部搜索间智能切换,从而保证全局与局部的平衡.动态变步长策略进一步增强了算法搜索能力.通过算例测试验证了所提出算法的有效性.
Aiming at the limited multi-objective flow shop scheduling problem with buffer, a generalized multi-target firefly algorithm based on Pareto optimality is proposed. The basic firefly algorithm is discretized by introducing commutator and exchange order, and the algorithm is extended to the global search process and local Search process.In the early stage of evolution, the global search was used to push the population to the superior region, and the predator-prey search strategy was used to make the main part of the algorithm switch intelligently between the global search and the local search so as to ensure the global and local balance. Which enhances the algorithm search ability.The validity of the proposed algorithm is verified by an example test.