Abstract
Differential evolution algorithm is a search and optimization strategy that simulates the process of biological evolution. In the initial stage of the algorithm, it is necessary to generate a series of deep neural networks with sufficient accuracy as the initial population of subsequent algorithms. In this article, an artificial bee colony search strategy is added to the cross-operation of the differential evolution algorithm to optimize the weight value. The artificial bee colony algorithm search operator is introduced to guide the search of the population to avoid individuals in the population from falling into a local optimal situation. The experiments in this article verify the validity of the method through the handwritten digit recognition data set. The final results show that in the process of obtaining the initial population, using the differential evolution weight optimization method of the artificial bee colony search strategy optimizes the process of the fitness calculation in the model. It significantly improves the accuracy of the first-generation population and speeds up the overall process of the algorithm.
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Wu, Y. et al. (2021). Weights Optimization Method of Differential Evolution Based on Artificial Bee Colony Algorithm. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_49
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DOI: https://doi.org/10.1007/978-3-030-72792-5_49
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