Improved Multi-Objective Cuckoo-Catfish Optimizer for Smooth and Collision-Free Mobile Robot Path Planning

In this paper, an Adaptive Improved Cuckoo-Catfish Optimizer (AICCO) is proposed for smooth and collision-free mobile robot path planning in static and dynamic environments. The proposed AICCO enhances the original Cuckoo-Catfish Optimizer (CCO) by integrating chaotic opposition-based initialization, nonlinear adaptive control, elite-guided search, strong elite preservation, memetic local refinement, and stagnation-based opposition repair. A weighted-sum scalarized fitness function is formulated to minimize path length and turning-angle variation while maximizing obstacle clearance. The proposed method is evaluated using 23 benchmark functions and further validated in static, dynamic, and nonlinear/reactive obstacle navigation scenarios. The benchmark results show that AICCO achieves the best overall rank among the optimizers compared. In the static planning scenario, the method achieves zero collision penalty and a minimum clearance of 0.785565. In the dynamic online replanning scenario, it achieves zero collisions with a minimum clearance of 1.514250. An additional nonlinear/reactive dynamic scenario further demonstrates that the proposed method can maintain collision-free naviga