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<p>In this work, an enhanced metaheuristic optimization algorithm, termed the Adaptive LightTrack Top-guided Cuckoo Catfish Optimizer (ALTCCO), is proposed to improve the performance of the original Cuckoo Catfish Optimizer (CCO) in solving complex numerical and real-world optimization problems. ALTCCO integrates three complementary strategies to reinforce population diversity, adaptive search, and convergence stability: (1) a bidirectional cross-interaction mechanism combining horizontal (dimension-wise) and vertical (segment-wise) crossover to enrich information exchange; (2) a LightTrack strategy incorporating historical position memory and stagnation-driven repulsive jumps to escape local optima; and (3) a top-guided adaptive mutation where mutation intensity is dynamically adjusted based on rank-based fitness to balance exploration and exploitation. ALTCCO was rigorously evaluated on 29 CEC2017 benchmark functions, five classic engineering design problems, and a high-dimensional 3D UAV path planning task in a complex constrained environment. Experimental results demonstrate that ALTCCO achieves superior convergence speed, optimization accuracy, and robustness across all test cases. On the CEC2017 benchmark suite, ALTCCO obtained the best results on 28 out of 29 benchmark functions and achieved the lowest average Friedman rank of 1.07 among thirteen competing algorithms. Additional high-dimensional experiments further confirmed its scalability, where ALTCCO maintained the best overall average ranks of 1.21 and 1.34 on the 50-dimensional and 100-dimensional CEC2017 benchmark sets, respectively. In the high-dimensional UAV path planning task, ALTCCO achieved the lowest mean path cost of 141.6121 with a standard deviation of only 2.2920, demonstrating excellent solution quality and stability. Statistical analyses based on the Friedman ranking and Wilcoxon signed-rank test further confirm the significant performance superiority of ALTCCO, establishing it as an efficient and versatile optimization framework for complex engineering applications.</p>

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ALTCCO: an enhanced cuckoo catfish optimizer with LightTrack strategy for engineering design and UAV trajectory optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Aolin Chen,
  • Ning Quan,
  • Shuo Yan

摘要

In this work, an enhanced metaheuristic optimization algorithm, termed the Adaptive LightTrack Top-guided Cuckoo Catfish Optimizer (ALTCCO), is proposed to improve the performance of the original Cuckoo Catfish Optimizer (CCO) in solving complex numerical and real-world optimization problems. ALTCCO integrates three complementary strategies to reinforce population diversity, adaptive search, and convergence stability: (1) a bidirectional cross-interaction mechanism combining horizontal (dimension-wise) and vertical (segment-wise) crossover to enrich information exchange; (2) a LightTrack strategy incorporating historical position memory and stagnation-driven repulsive jumps to escape local optima; and (3) a top-guided adaptive mutation where mutation intensity is dynamically adjusted based on rank-based fitness to balance exploration and exploitation. ALTCCO was rigorously evaluated on 29 CEC2017 benchmark functions, five classic engineering design problems, and a high-dimensional 3D UAV path planning task in a complex constrained environment. Experimental results demonstrate that ALTCCO achieves superior convergence speed, optimization accuracy, and robustness across all test cases. On the CEC2017 benchmark suite, ALTCCO obtained the best results on 28 out of 29 benchmark functions and achieved the lowest average Friedman rank of 1.07 among thirteen competing algorithms. Additional high-dimensional experiments further confirmed its scalability, where ALTCCO maintained the best overall average ranks of 1.21 and 1.34 on the 50-dimensional and 100-dimensional CEC2017 benchmark sets, respectively. In the high-dimensional UAV path planning task, ALTCCO achieved the lowest mean path cost of 141.6121 with a standard deviation of only 2.2920, demonstrating excellent solution quality and stability. Statistical analyses based on the Friedman ranking and Wilcoxon signed-rank test further confirm the significant performance superiority of ALTCCO, establishing it as an efficient and versatile optimization framework for complex engineering applications.