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Building an Enhanced Flight Mill for the Study of Tethered Insect Flight

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Published on: March 10, 2021

Multi-strategy enhanced black-winged kite algorithm and its engineering applications.

Meijin Lin¹, Zhirong Qiu², Weijia Zheng¹

¹School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528200, China.

Scientific Reports

|April 18, 2026

Summary

This summary is machine-generated.

The Multi-strategy Enhanced Black-winged Kite Algorithm (MSEBKA) improves upon the original BKA by incorporating adaptive inertia weight, elite pool, dynamic Gaussian random walk, and lens imaging opposition-based learning strategies. This enhanced algorithm demonstrates superior performance in complex optimization problems and constrained engineering designs.

Keywords:

Adaptive inertia weight Black-winged kite algorithm Dynamic Gaussian random walk Elite pool Engineering applications Lens imaging opposition-based learning

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristic Computing

Background:

The Black-winged Kite Algorithm (BKA) is a metaheuristic optimization technique.
BKA exhibits limitations in premature convergence and slow speed for complex problems.

Purpose of the Study:

To enhance the Black-winged Kite Algorithm (BKA) for improved performance on complex optimization tasks.
To introduce a Multi-strategy Enhanced Black-winged Kite Algorithm (MSEBKA) addressing BKA's limitations.

Main Methods:

Incorporation of an adaptive inertia weight (AIW) strategy in the attack phase.
Integration of an elite pool (EP) strategy during the migration phase.
Application of dynamic Gaussian random walk (DGRW) and lens imaging opposition-based learning (LIOBL) strategies for enhanced exploration and exploitation.

Main Results:

MSEBKA outperformed BKA and its variants on CEC2019 benchmark functions in terms of mean fitness and standard deviation.
MSEBKA achieved superior overall performance and better Friedman mean ranks across CEC2019, classic, and CEC2021 benchmark functions.
MSEBKA demonstrated improved performance on constrained engineering design problems, including pressure vessel and spring design.

Conclusions:

The proposed MSEBKA effectively addresses the premature convergence and slow convergence speed issues of the original BKA.
MSEBKA exhibits robust performance across various benchmark functions and practical engineering optimization problems.
The integrated strategies significantly enhance the algorithm's ability to escape local optima and accelerate convergence.