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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The multiple knapsack problem（MKP） is a special knapsack problem with great difficulty. In order to solve MKP by differential evolution（DE）, this paper first established the integer programming model of MKP. Based on a simple and efficient new transfer function based on modulo operation, it proposed a novel discrete differential evolution algorithm MODDE. Then, the method used an efficient algorithm GROA to eliminate the unfeasible solution of MKP by greedy strategy. Therefrom, this paper proposed a new method for solving MKP based on MODDE. Finally, it used MODDE to solve 30 international instances of MKP. Comparing with 4 representative evolution algorithms shows that MODDE not only has better calculation results; but also has stronger stability. It is indeed an efficient algorithm for solving MKP [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SNN is a novel biologically interpretable network model. In response to the problem that traditional SNN models have inadequate representation ability and cannot apply to practical tasks, this paper studied the capability of SNN to process EEG recognition tasks and developed a SNN model with long short-term memory structure. Firstly, this paper adopted the improved BSA encoding algorithm to process the EEG signals. Secondly, this paper constructed the spiking neuron with an adaptive threshold and established the SNN model with LSTM structure using the PyTorch framework. Finally, this paper utilized the surrogate gradient method to overcome the non-differentiable problem of spike activity and directly trained the SNN based on backpropagation while retaining the dynamic nature of the neuron. Simulation experiments show that the improved BSA is more flexible and reliable. Simultaneously, the SNN model with LSTM structure improves the representation ability and achieves an accuracy that can compete with traditional deep learning model in EEG recognition tasks. [ABSTRACT FROM AUTHOR]