关键词: 回声状态网络, 储层, 多策略鲸鱼优化算法, 参数优化, 池化机制, 搜索策略

Abstract: To address poor network prediction performance caused by the randomness of reservoir parameter selection in traditional echo state network (ESN), this paper proposes an ESN parameter optimization model based on multi-strategy whale optimization algorithm (MWOA-ESN). The key parameters of ESN reservoir are optimized by MWOA algorithm. By introducing the pooling mechanism, migration strategy and priority selection strategy, MWOA effectively solves the defects of low population diversity and easy to fall into local optima of the whale optimization algorithm, and improves the optimization efficiency. Through simulation experiments on multiple time series data sets and short-term power load data sets, the results show that the proposed MWOA-ESN model has universality, and outperforms the existing classical models in terms of prediction accuracy and fitting. Compared with the existing results, the MWOA-ESN parameter optimization model is feasible and effective.

Key words: echo state network (ESN), reservoir, multi-strategy whale optimization algorithm, parameter optimization, pooling mechanism, search strategy