With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood search. However, the local search (LS) operators are inefficient due to strong randomness; 2) the confidence-based adaptive operator selection model follows the experiences of the major crowds, which ignores the efficient operators with low weight, so it can not select the really efficient operator; 3) the previous works lack of efficient strategy to save energy; and 4) the mainstream memetic framework adopts LS to all solutions, which causes the population to converge too quickly and the diversity is extremely reduced. Thus, we propose a surprisingly popular-based adaptive MA (SPAMA) to overcome the above deficiencies. The contributions are as follows: 1) four problem-based LS operators are employed to improve the convergence; 2) a surprisingly popular degree (SPD) feedback-based self-modifying operators selection model is proposed to find the efficient operators with low weight and correct crowd decision making; 3) the full active scheduling decoding is presented to reduce the energy consumption; and 4) an elite strategy is designed to balance the resources between global and LS. In order to evaluate the effectiveness of SPAMA, it is compared with state-of-the-art algorithms on Mk and DP benchmarks. The results demonstrate the superiority of SPAMA to the state-of-art algorithms for solving EDFJSP.

中文翻译：

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令人惊讶的流行的自适应模因算法，用于节能分布式灵活作业车间调度

随着经济的发展，分布式制造逐渐成为主流生产模式。这项工作旨在解决节能分布式灵活作业车间调度问题（EDFJSP），同时最小化完工时间和能源消耗。一些差距如下： 1）以前的工作通常采用具有可变邻域搜索的模因算法（MA）。然而，局部搜索（LS）算子由于随机性强而效率低下；2）基于置信度的自适应算子选择模型遵循主要人群的经验，忽略了权重较低的高效算子，因此无法选择真正高效的算子；3）以往的工作缺乏有效的节能策略；4）主流模因框架对所有解都采用LS，导致种群收敛过快，多样性极度降低。因此，我们提出了一种基于流行的自适应 MA (SPAMA) 来克服上述缺陷。贡献如下：1）采用四个基于问题的LS算子来提高收敛性；2）提出了一种基于流行度（SPD）反馈的自修正算子选择模型，以找到低权重和正确群体决策的高效算子；3）提出全主动调度解码，降低能耗；4）精英战略旨在平衡全球和LS之间的资源。为了评估 SPAMA 的有效性，我们将其与 Mk 和 DP 基准上最先进的算法进行了比较。结果证明了 SPAMA 相对于解决 EDFJSP 的最先进算法的优越性。