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<p>As an effective alternative model to recurrent neural network (RNN), echo state network (ESN) has garnered more attention due to its efficiency in handling time series data. Despite the simple training process and rapid convergence speed of ESN, appropriate parameter settings and a concise network structure are crucial for optimal model performance. Therefore, many optimization algorithms have been proposed to obtain the optimal parameters of ESN. Among these methods, the Pigeon-Inspired Optimization (PIO) has gained attention due to its fast search speed, strong evolution capability, and excellent optimization ability. However, the main drawbacks of PIO are that it may easily get trapped in local optima and achieve lower precision results. To address these issues, this paper proposes a hybrid algorithm combining adaptive improved pigeon-inspired optimization with tabu search (TS-APIO) algorithm. By combining the improved PIO and the tabu search (TS), it not only enhances the global search capability but also strengthens its robustness. Additionally, the adaptive adjustment mechanism can improve the generalization ability. Through theoretical analysis and simulation examples, the TS-APIO algorithm can adaptively select the optimal ESN parameters and structure based on different scenarios. It can effectively enhance the ability to capture the dynamic features and reduce the prediction error.</p>

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An echo state network with adaptive improved pigeon-inspired optimization for time series prediction

  • 期刊论文

  • 发表日期：

    2025年2月12日

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  • Xu Yang,
  • Lei Wang,
  • Qili Chen

摘要

As an effective alternative model to recurrent neural network (RNN), echo state network (ESN) has garnered more attention due to its efficiency in handling time series data. Despite the simple training process and rapid convergence speed of ESN, appropriate parameter settings and a concise network structure are crucial for optimal model performance. Therefore, many optimization algorithms have been proposed to obtain the optimal parameters of ESN. Among these methods, the Pigeon-Inspired Optimization (PIO) has gained attention due to its fast search speed, strong evolution capability, and excellent optimization ability. However, the main drawbacks of PIO are that it may easily get trapped in local optima and achieve lower precision results. To address these issues, this paper proposes a hybrid algorithm combining adaptive improved pigeon-inspired optimization with tabu search (TS-APIO) algorithm. By combining the improved PIO and the tabu search (TS), it not only enhances the global search capability but also strengthens its robustness. Additionally, the adaptive adjustment mechanism can improve the generalization ability. Through theoretical analysis and simulation examples, the TS-APIO algorithm can adaptively select the optimal ESN parameters and structure based on different scenarios. It can effectively enhance the ability to capture the dynamic features and reduce the prediction error.