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<p>This paper aims to address the problems of low initial population diversity and being prone to falling into a local optimum in the basic Black-winged kite optimization algorithm (BKA) and proposes an improved multi-strategy hybrid black-winged kite optimization algorithm (IMBKA) and its application. Firstly, in the process of generating the initial group, the optimal point set model is adopted for optimization; Secondly, an adaptive weighting method has been added to the attack behavior; Then, alert behaviors that can significantly enhance the robustness and optimize the performance of the algorithm were introduced; Finally, the Levy flight strategy was combined with the migration behavior to prevent the algorithm from becoming trapped in a local optimum. In this study, the Markov chain was constructed to prove the convergence of the improved algorithm, and a test function was used to conduct a comparative test of IMBKA with five other algorithms. The results demonstrate that the performance of the improved algorithm surpasses that of the other algorithms. In practical applications, a model for predicting pantograph-catenary contact resistance is constructed by optimizing the parameters of the Support Vector Machine (SVM) through IMBKA. The model’s prediction results further demonstrate that the improved algorithm is practical.</p>

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Improved black-winged kite optimization algorithm with multi-strategy hybrid and its application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月30日

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  • Lichuan Hui,
  • Yixiang Kong

摘要

This paper aims to address the problems of low initial population diversity and being prone to falling into a local optimum in the basic Black-winged kite optimization algorithm (BKA) and proposes an improved multi-strategy hybrid black-winged kite optimization algorithm (IMBKA) and its application. Firstly, in the process of generating the initial group, the optimal point set model is adopted for optimization; Secondly, an adaptive weighting method has been added to the attack behavior; Then, alert behaviors that can significantly enhance the robustness and optimize the performance of the algorithm were introduced; Finally, the Levy flight strategy was combined with the migration behavior to prevent the algorithm from becoming trapped in a local optimum. In this study, the Markov chain was constructed to prove the convergence of the improved algorithm, and a test function was used to conduct a comparative test of IMBKA with five other algorithms. The results demonstrate that the performance of the improved algorithm surpasses that of the other algorithms. In practical applications, a model for predicting pantograph-catenary contact resistance is constructed by optimizing the parameters of the Support Vector Machine (SVM) through IMBKA. The model’s prediction results further demonstrate that the improved algorithm is practical.