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<p>The Black-winged Kite Optimization Algorithm (BKA) is an innovative and effective meta-heuristic algorithm; however, it exhibits drawbacks such as low population diversity, insufficient search accuracy, slow convergence speed, and inadequate ability to escape local optima. Therefore, this paper proposes an Improved Black-winged Kite Optimization Algorithm with Multi-strategy Enhancement (FBKA) to address numerical optimization and engineering design problems. FBKA employs three strategies: the Multi-strategy Chaotic System (LTCS), Adaptive Dimension Complementary Mechanism (ADC), and simulation of organisms inhabiting Fish Aggregating Devices (FADs). These strategies effectively enhance the algorithm’s convergence speed and accuracy, reduce the risk of trapping in local optima, and achieve balanced exploration and exploitation. The performance of FBKA was benchmarked against state-of-the-art algorithms using the CEC-2017 and CEC-2022 benchmark suites across five different dimensions, with results analyzed for 111 numerical optimization problems. Compared to the original BKA, FBKA achieved an average improvement of 38% in convergence accuracy and 72% in stability across different dimensions in the CEC-2017 benchmark function tests. In the CEC-2022 benchmark function tests, it showed an average improvement of 21% in convergence accuracy and 79% in stability across different dimensions. In addition, FBKA outperformed its competitors in solving five practical engineering design problems, verifying its potential in addressing real-world constrained problems and demonstrating its practical value and advantages.</p>

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FBKA: an improved black-winged kite algorithm with multi-strategy enhancement for constrained engineering optimization

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Zhihong Wang,
  • Feng Chen,
  • Junhong Xiao

摘要

The Black-winged Kite Optimization Algorithm (BKA) is an innovative and effective meta-heuristic algorithm; however, it exhibits drawbacks such as low population diversity, insufficient search accuracy, slow convergence speed, and inadequate ability to escape local optima. Therefore, this paper proposes an Improved Black-winged Kite Optimization Algorithm with Multi-strategy Enhancement (FBKA) to address numerical optimization and engineering design problems. FBKA employs three strategies: the Multi-strategy Chaotic System (LTCS), Adaptive Dimension Complementary Mechanism (ADC), and simulation of organisms inhabiting Fish Aggregating Devices (FADs). These strategies effectively enhance the algorithm’s convergence speed and accuracy, reduce the risk of trapping in local optima, and achieve balanced exploration and exploitation. The performance of FBKA was benchmarked against state-of-the-art algorithms using the CEC-2017 and CEC-2022 benchmark suites across five different dimensions, with results analyzed for 111 numerical optimization problems. Compared to the original BKA, FBKA achieved an average improvement of 38% in convergence accuracy and 72% in stability across different dimensions in the CEC-2017 benchmark function tests. In the CEC-2022 benchmark function tests, it showed an average improvement of 21% in convergence accuracy and 79% in stability across different dimensions. In addition, FBKA outperformed its competitors in solving five practical engineering design problems, verifying its potential in addressing real-world constrained problems and demonstrating its practical value and advantages.