Modified Black-Winged Kite Optimization Algorithm with Three-Phase Attacking Strategy and Lévy-Cauchy Migration...

Area of Science:

• Computational Intelligence
• Heuristic Optimization Algorithms

Background:

• The Black-winged Kite Algorithm (BKA) is a recent heuristic optimization method.
• Existing algorithms require further enhancements in convergence accuracy and solution quality.

Purpose of the Study:

• To introduce a Modified Black-winged Kite Algorithm (MBKA).
• To improve the convergence accuracy and solution quality of the BKA.

Main Methods:

• Developed a three-phase attacking strategy to boost population diversity.
• Integrated a Lévy-Cauchy migration strategy for better exploration-exploitation balance.
• Evaluated MBKA on benchmark functions (CEC-2017, CEC-2022) and engineering problems.

Main Results:

• MBKA demonstrated enhanced population diversity and solution quality.
• The Lévy-Cauchy strategy improved the exploration-exploitation trade-off.
• MBKA consistently outperformed the original BKA and other state-of-the-art algorithms.

Conclusions:

• MBKA offers superior performance in convergence accuracy and speed.
• The proposed modifications significantly enhance the BKA's optimization capabilities.
• MBKA is a promising algorithm for complex optimization tasks.