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<p>The Black Kite Algorithm (BKA) is a nature-inspired meta-heuristic algorithm designed to mimic the migratory and predatory behaviours of the black kite. However, the parameter adjustments and heavy dependence on the previous generation’s position update strategy in BKA can negatively affect the stability of optimization results. This paper introduces an improved algorithm named ALBKA to address these limitations. ALBKA enhances population diversity during initialization by employing an improved Tent chaotic map with increased randomness and uniformity. It integrates dynamic adjustments of search amplitudes and adaptively modifies neighbourhood and global guidance factors. Furthermore, a Lévy flight strategy is introduced to help the algorithm escape local optima, while an elite guidance mechanism, selecting superior-performing solutions, ensures consistent convergence toward better solutions. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark functions and practical engineering optimization problems demonstrate that ALBKA achieved the best performance in 85.36% of the 41 tested benchmark functions, representing an absolute improvement of nearly 49 percentage points over the original BKA (36.57%). Additionally, the standard deviation was significantly reduced, indicating improved stability of optimization results. ALBKA exhibits higher solution accuracy and superior practical utility, especially in practical applications such as path planning. These results demonstrate that ALBKA outperforms the original BKA and several advanced swarm intelligence algorithms.</p>

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Path planning problem solved by an improved black-winged kite optimization algorithm based on multi-strategy fusion

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Chenxing Li,
  • Keyuan Zhang,
  • Bin Zheng,
  • Yanguang Chen

摘要

The Black Kite Algorithm (BKA) is a nature-inspired meta-heuristic algorithm designed to mimic the migratory and predatory behaviours of the black kite. However, the parameter adjustments and heavy dependence on the previous generation’s position update strategy in BKA can negatively affect the stability of optimization results. This paper introduces an improved algorithm named ALBKA to address these limitations. ALBKA enhances population diversity during initialization by employing an improved Tent chaotic map with increased randomness and uniformity. It integrates dynamic adjustments of search amplitudes and adaptively modifies neighbourhood and global guidance factors. Furthermore, a Lévy flight strategy is introduced to help the algorithm escape local optima, while an elite guidance mechanism, selecting superior-performing solutions, ensures consistent convergence toward better solutions. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark functions and practical engineering optimization problems demonstrate that ALBKA achieved the best performance in 85.36% of the 41 tested benchmark functions, representing an absolute improvement of nearly 49 percentage points over the original BKA (36.57%). Additionally, the standard deviation was significantly reduced, indicating improved stability of optimization results. ALBKA exhibits higher solution accuracy and superior practical utility, especially in practical applications such as path planning. These results demonstrate that ALBKA outperforms the original BKA and several advanced swarm intelligence algorithms.