A Novel Metaheuristic Approach: Spiral Cloud Optimization Algorithm

This study introduces a novel meta-heuristic algorithm named Spiral Cloud Optimization Algorithm (SCOA), inspired by the movement patterns of clouds. SCOA is mathematically modeled based on the optimal motion of clouds in nature to perform optimization across a wide range of search spaces. The core concept of this algorithm is derived from the spiral behavior of clouds and the Fibonacci sequence. This algorithm is distinguished by its high-speed performance, simplicity of implementation, and impressive convergence. Moreover, the golden ratio, a mathematical principle, is incorporated into the algorithm. The efficiency of SCOA is attributed to its streamlined processes, making it particularly suitable for tasks that require rapid execution and reliable convergence. The combination of speed and simplicity makes it an appealing choice for scenarios with limited computational resources or a need for quick results. The proposed algorithm is evaluated using 68 benchmark functions and two engineering problems. The results demonstrate that SCOA provides superior performance in terms of precision and convergence speed when solving complex optimization problems, outperforming other algorithms such as Artificial Bee Colony (ABC), Gray Wolf Optimizer (GWO), Fire Hawks Optimizer (FHO), and Flying Fox Optimization (FFO), among others.