Improved Superb Fairy-Wren Optimization Algorithm and Its Application.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Superb Fairy-wren Optimization Algorithm (SFOA) faces limitations in convergence accuracy and local optima for complex problems.
• Meta-heuristic algorithms require continuous improvement to address challenging optimization tasks effectively.

Purpose of the Study:

• To introduce an Improved Superb Fairy-wren Optimization Algorithm (ISFOA) that overcomes the drawbacks of the conventional SFOA.
• To enhance the balance between global exploration and local exploitation in meta-heuristic optimization.

Main Methods:

• Incorporation of Chebyshev chaotic mapping, adaptive weighting factor, Cauchy-Gaussian mutation, and t-distribution perturbation into SFOA.
• Performance evaluation using ablation studies on the CEC 2021 test suite.
• Comparative analysis against eight other meta-heuristic algorithms on CEC2005 and CEC2021 benchmark functions and seven engineering design problems.

Main Results:

• The ablation study confirmed the effectiveness of each incorporated strategy in ISFOA.
• ISFOA demonstrated superior performance over SFOA and other compared algorithms in benchmark tests.
• ISFOA showed significant advantages in robustness and convergence accuracy on engineering design problems.

Conclusions:

• ISFOA offers an efficient and reliable approach for solving complex optimization problems.
• The proposed enhancements effectively improve the balance between exploration and exploitation.
• ISFOA presents a promising alternative for various optimization challenges in science and engineering.