Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design.

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Bibliographic Details
Title:	Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design.
Authors:	Xiong, Xiangnan¹ 1206017946@qq.com, Yu, Peng² oykunpeng@163.com, Chen, Xuebo³ xuebochen@126.com
Source:	Engineering Letters. Feb2026, Vol. 34 Issue 2, p664-685. 22p.
Subjects:	Robotic path planning, Engineering design, Optimization algorithms, Differential evolution, Metaheuristic algorithms
Abstract:	The metaheuristic optimization algorithm finds broad applications in fields such as machine learning, engineering design, and control. The Pied Kingfisher Optimizer, emerging in recent years, is an efficient metaheuristic algorithm that draws inspiration from the distinctive hunting strategies and symbiotic interactions of the pied kingfisher observed in their natural environment. However, it still grapples with sluggish convergence and a tendency to become trapped in local optima when tackling specific complex problems. To counter these drawbacks, this study develops a modified Pied Kingfisher Optimizer. This improvement integrates three hybrid mechanisms: the Firefly Algorithm, Differential Evolution, and an adaptive t-distribution mutation operator. Ablation testing via the CEC2017 and CEC2022 suites evaluated the operational capability of the proposed algorithm. Furthermore, the upgraded algorithm was rigorously benchmarked against several other metaheuristic optimizers. Empirical findings indicate that the refined Pied Kingfisher Optimizer achieves a higher overall performance level compared to the chosen competitor algorithms. This performance superiority was statistically confirmed using the Friedman test. Additionally, to validate the practical utility of the Pied Kingfisher Optimizer with hybrid strategy improvement in control and engineering domains, experiments were conducted on robot path planning and engineering design problems, where it demonstrated commendable performance. These results further confirm the superiority of the improved PKO algorithm in the fields of control systems and engineering design. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	The metaheuristic optimization algorithm finds broad applications in fields such as machine learning, engineering design, and control. The Pied Kingfisher Optimizer, emerging in recent years, is an efficient metaheuristic algorithm that draws inspiration from the distinctive hunting strategies and symbiotic interactions of the pied kingfisher observed in their natural environment. However, it still grapples with sluggish convergence and a tendency to become trapped in local optima when tackling specific complex problems. To counter these drawbacks, this study develops a modified Pied Kingfisher Optimizer. This improvement integrates three hybrid mechanisms: the Firefly Algorithm, Differential Evolution, and an adaptive t-distribution mutation operator. Ablation testing via the CEC2017 and CEC2022 suites evaluated the operational capability of the proposed algorithm. Furthermore, the upgraded algorithm was rigorously benchmarked against several other metaheuristic optimizers. Empirical findings indicate that the refined Pied Kingfisher Optimizer achieves a higher overall performance level compared to the chosen competitor algorithms. This performance superiority was statistically confirmed using the Friedman test. Additionally, to validate the practical utility of the Pied Kingfisher Optimizer with hybrid strategy improvement in control and engineering domains, experiments were conducted on robot path planning and engineering design problems, where it demonstrated commendable performance. These results further confirm the superiority of the improved PKO algorithm in the fields of control systems and engineering design. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X