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

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Complex numerical and real-world optimization problems present significant challenges.
• Existing metaheuristic algorithms often struggle with population diversity, local optima, and convergence stability.

Purpose of the Study:

• To introduce an enhanced metaheuristic optimization algorithm, Adaptive LightTrack Top-guided Cuckoo Catfish Optimizer (ALTCCO).
• To improve upon the original Cuckoo Catfish Optimizer (CCO) for complex optimization tasks.

Main Methods:

• ALTCCO integrates bidirectional cross-interaction, a LightTrack strategy with historical memory and repulsive jumps, and top-guided adaptive mutation.
• The algorithm was evaluated on 29 CEC2017 benchmark functions, engineering design problems, and a 3D UAV path planning task.

Main Results:

• ALTCCO achieved superior convergence speed, accuracy, and robustness across all test cases.
• It secured the best results on 28 of 29 CEC2017 functions and demonstrated strong scalability in high-dimensional scenarios.
• ALTCCO achieved the lowest mean path cost in the UAV path planning task, indicating excellent solution quality and stability.

Conclusions:

• ALTCCO significantly outperforms existing algorithms, establishing itself as an efficient and versatile optimization framework.
• The proposed strategies effectively enhance population diversity, adaptive search, and convergence stability for complex engineering applications.