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Published on: October 14, 2017

An Enhanced Black-Winged Kite Algorithm with Multiple Strategies for Global Optimization and Constrained Engineering

Chengtao Du1, Jinzhong Zhang1, Jie Fang1

  • 1School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

The enhanced black-winged kite algorithm (BKA) improves convergence and precision by integrating multiple strategies. This optimized algorithm (MSBKA) achieves superior global optimization for engineering applications.

Keywords:
benchmark functionsblack-winged kite algorithmelite opposition-based learning strategyengineering designsranking-based differential mutationsimplex method

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Published on: October 14, 2017

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The standard black-winged kite algorithm (BKA) faces challenges with slow convergence, low precision, and limited population diversity.
  • These limitations hinder its effectiveness in complex global optimization and constrained engineering problems.

Purpose of the Study:

  • To enhance the black-winged kite algorithm (BKA) by incorporating multiple strategies, creating the MSBKA.
  • To improve convergence speed, computational precision, and population diversity for better global optimization.
  • To validate the MSBKA's performance on benchmark functions and real-world engineering designs.

Main Methods:

  • Implemented ranking-based differential mutation to enhance population interaction and accelerate convergence.
  • Integrated the simplex method for local refinement, improving solution accuracy and avoiding local optima.
  • Utilized elite opposition-based learning to expand search space and boost population diversity, preventing premature convergence.
  • Developed a multi-strategy enhanced BKA (MSBKA) with deficiency orientation, strategy adaptation, and collaborative search.

Main Results:

  • The MSBKA demonstrated significant improvements in convergence efficiency and solution accuracy compared to the standard BKA.
  • Experimental results on 23 benchmark functions and 6 engineering designs confirmed the MSBKA's superior performance.
  • The enhanced algorithm effectively addressed issues like search stagnation, convergence oscillation, and premature convergence.

Conclusions:

  • The MSBKA offers a robust and efficient approach for global optimization and constrained engineering applications.
  • It successfully overcomes the limitations of the original BKA, providing high precision and efficiency.
  • The MSBKA exhibits strong practicability, reliability, and stability, making it suitable for demanding optimization tasks.