Multi-Strategy Boosted Fick's Law Algorithm for Engineering Optimization Problems and Parameter Estimation.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• The Fick's Law Algorithm faces limitations in local convergence and efficiency.
• Need for robust optimization methods in complex engineering problems.

Purpose of the Study:

• To propose a multi-strategy improved Fick's Law Algorithm (FLAS).
• To enhance convergence efficiency and exploration capabilities.
• To validate FLAS performance on benchmark functions and engineering problems.

Main Methods:

• Integration of differential mutation, Gaussian local mutation, comprehensive learning, and seagull update strategies.
• Validation using 23 benchmark functions and CEC2020 test suite.
• Application to seven engineering optimization problems and solar PV model parameter estimation.

Main Results:

• FLAS exhibits improved exploration and exploitation capabilities.
• Significant performance gains compared to other algorithms in engineering optimizations.
• Effective parameter estimation for solar PV models, demonstrating practical applicability.

Conclusions:

• The proposed FLAS effectively addresses the shortcomings of the original Fick's Law Algorithm.
• FLAS shows strong potential for solving complex engineering optimization problems.
• The algorithm's practical engineering applicability is confirmed through solar model analysis.