Considering green scheduling and sustainable manufacturing, the energy-efficient hybrid flow shop scheduling problem (EHFSP) with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm (MPABC) is developed to minimize makespan, total tardiness and total energy consumption (TEC), simultaneously. It is necessary for manufacturers to fully understand the notion of symmetry in balancing economic and environmental indicators. To improve the search efficiency, the population was randomly categorized into a number of subpopulations, then several groups were constructed based on the quality of subpopulations. A different search strategy was executed in each group to maintain the population diversity. The historical optimization data were also used to enhance the quality of solutions. Finally, extensive experiments were conducted. The results demonstrate that MPABC can achieve an outstanding performance on three metrics ${DI}_R$, $c$ and $nd$ for the considered EHFSP.

Assembly shop scheduling and energy-efficient scheduling have attracted much attention in the past decades; however, energy consumption is often ignored in assembly hybrid flow shop scheduling. Neglecting energy consumption will greatly diminish the progress of sustainable manufacturing. In this study, an assembly hybrid flow shop scheduling problem considering energy consumption (EAHFSP) is investigated, and a population diversity-based artificial bee colony algorithm (DABC) is proposed to minimize the makespan and total energy consumption (TEC) simultaneously. Diversified search strategies based on rank value are introduced to the employed bee phase; a novel probability selection method in the onlooker bee phase is designed to control the selection pressure; moreover, a diversity control strategy is applied to improve the diversity of food sources and avoid falling into stagnation. A number of experiments based on 44 extended benchmark instances from the literature and a real case are conducted to test the performance of the DABC algorithm. The statistical results show that the DABC algorithm is superior to the other four state-of-the-art algorithms on over 70% of the instances corresponding to metrics $IGD$ and $c$, which means that the DABC algorithm is effective and competitive in solving the considered EAHFSP.

Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has many hyperparameters that are difficult to determine, making the model easily fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithms are used to solve the above problems. However, these algorithms perform poorly compared to the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers for the first time, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the average accuracy of DBO-CNN has increased by 1.22~4.39% and reached 97.93%.

Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has a large number of hyperparameters that are difficult to be determined, which makes the model easy to fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithm are used to solve the above problems. However, these algorithms have poor performance compared with the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the accuracy of DBO-CNN has increased by 0.6%~4.8%, and reached 98.3%.

This paper introduces an automatic bond graph design method based on genetic programming for the evolutionary design of micro-resonator. First, the system-level behavioral model is discussed, which based on genetic programming and bond graph. Then, the geometry parameters of components are automatically optimized, by using the genetic algorithm with constraints. To illustrate this approach, a typical device micro-resonator is designed as an example in biomedicine. This paper provides a new idea for the automatic optimization design of biomedical sensors by evolutionary calculation.