A novel reindeer cyclone optimization algorithm (RCOA)

This paper introduces the Reindeer Cyclone Optimization Algorithm (RCOA), a novel metaheuristic optimization technique inspired by the survival behavior of reindeer during predator attacks in formation cyclonic storms. RCOA imitates the defense-centric cooperative behavior of reindeer, where individuals cluster together to withstand external threats. This behavior is analogous to the optimization process where exploration (global search for exploring new areas) and exploitation (local refinement to copy or learn from neighbor in cyclonic form) are carefully balanced. The algorithm has been extensively evaluated against 14 unimodal and multimodal benchmark functions and 4 real-world complex optimization problems. RCOA demonstrates a moderate improvement of around 5-12% over other algorithms such as PSO, DE, COA and GSA on unimodal functions. On multimodal functions, RCOA shows more competitive performance, especially in terms of stability, with an improvement of around 10-15% in accuracy and consistency compared to WDO and PSO. The algorithm is evaluated using the CEC'17 benchmark suite with 50 dimensions and compared against different well-established optimization algorithms, including WOA, PSO, GSA, and DE. Experimental results demonstrate that RCOA outperforms existing methods on multiple test functions by achieving superior convergence speed and solution accuracy. The Wilcoxon Signed-Rank test confirms the statistical significance of RCOA's performance, indicating its robustness and reliability in handling diverse optimization landscapes. The findings suggest that RCOA is a competitive optimization method suitable for a wide range of real-world applications.