A multi-strategy integrated improved nutcracker optimization algorithm and its applications in water conservancy...

Area of Science:

• Engineering
• Computer Science
• Artificial Intelligence

Background:

• Complex optimization problems in water conservancy engineering require advanced algorithms.
• The nutcracker optimization algorithm (NOA) shows promise but has limitations.
• Existing meta-heuristic algorithms need further enhancement for practical applications.

Purpose of the Study:

• To improve the nutcracker optimization algorithm (NOA) for enhanced performance in water conservancy engineering.
• To address the limitations of NOA by incorporating novel strategies.
• To evaluate the effectiveness of the improved NOA (INOA) in real-world engineering applications.

Main Methods:

• The improved NOA (INOA) was developed by integrating logistic-tent chaotic mapping, tangent flight strategy, dynamic fitness-distance balance selection, criss-cross mutation, Gaussian mutation perturbation, and Tent chaotic perturbation.
• Performance analysis of 26 meta-heuristic algorithms using benchmark functions.
• Application of INOA to optimize an attention mechanism long short-term memory neural network (ATLSTM) for broken wire signal identification in prestressed concrete cylinder pipes (PCCP).
• Application of INOA to optimize the hydraulic-seasonal-time (HST) model for dam deformation prediction.

Main Results:

• The nutcracker optimization algorithm (NOA) demonstrated optimal performance among 26 selected algorithms.
• The improved NOA (INOA) significantly enhanced optimization accuracy, convergence speed, stability, and operational efficiency.
• INOA outperformed other baseline optimizers in both PCCP broken wire identification and dam deformation prediction.

Conclusions:

• The developed INOA is a robust and efficient optimization framework for water conservancy engineering.
• INOA shows significant potential for intelligent monitoring and prediction tasks in civil and hydraulic engineering.
• The enhanced optimization capabilities of INOA offer a valuable tool for addressing complex engineering challenges.