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

Yunpeng Ma¹, Wanting Meng², Ruixue Gu¹

¹School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China.

Biomimetics (Basel, Switzerland)

|October 28, 2025

Summary

This summary is machine-generated.

The Modified Black-winged Kite Algorithm (MBKA) enhances optimization by improving population diversity and balancing exploration. This novel algorithm shows superior performance over the original BKA on benchmark and engineering problems.

Keywords:

CEC testing function Lévy–Cauchy operator black-winged kite optimization algorithm three-phase attacking strategy

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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.