Adolescent Identity Search Algorithm Based on Fast Search and Balance Optimization for Numerical and Engineering...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Design

Background:

• Traditional optimization algorithms often struggle with balancing exploration and exploitation.
• Local optima can hinder the accuracy and convergence speed of existing search strategies.
• Improving the efficiency of numerical and engineering design problem-solving is crucial.

Purpose of the Study:

• To introduce a novel optimization algorithm, the Fast Convergence and Balanced Adolescent Identity Search Algorithm (FCBAISA).
• To enhance the performance of numerical and engineering design by improving convergence speed and solution accuracy.
• To address the limitations of existing algorithms in avoiding local optima.

Main Methods:

• Implementation of a hierarchical optimization strategy for balanced exploration and exploitation.
• Development of a fast search strategy incorporating Brownian motion for enhanced guidance.
• Improvement of the Chebyshev functional-link network (CFLN) using recursive least squares estimation (RSLE).
• Integration of a terminal bounce strategy to prevent late-stage local optimization.

Main Results:

• FCBAISA demonstrated superior performance compared to other algorithms across CEC2017 and CEC2022 benchmark functions.
• The algorithm achieved high precision and fast convergence speeds in numerical optimization tasks.
• FCBAISA effectively solved practical engineering design problems, showcasing its real-world applicability.

Conclusions:

• FCBAISA offers significant advantages over existing optimization algorithms.
• The proposed strategies effectively improve convergence speed, accuracy, and robustness.
• FCBAISA presents a powerful tool for complex numerical and engineering design challenges.