Cloud drift optimization algorithm as a nature-inspired metaheuristic
Mohammad Alibabaei Shahraki
Abstract
Abstract This study introduces the Cloud Drift Optimization (CDO) algorithm, an innovative nature-inspired metaheuristic approach to solving complex optimization problems. The CDO algorithm mimics the dynamic behavior of cloud particles influenced by atmospheric forces, striking a refined balance between exploration and exploitation. It features an adaptive weight adjustment mechanism that alters the cloud’s real-time drift behavior, allowing for efficient navigation through the search space. Using a cloud-based drift strategy, CDO harnesses probabilistic movements to maneuver through the optimization landscape more effectively. The algorithm has undergone rigorous testing against various established unimodal and multimodal benchmark functions, where it showcases outstanding performance characterized by faster convergence rates, high robustness, and exceptional solution accuracy compared to top contemporary optimization techniques. Additionally, CDO applies to numerous real-world engineering optimization tasks, such as designing cantilever beams, three-bar trusses, tension/compression springs, and pressure vessels. The empirical data highlight CDO’s ability to deliver innovative solutions across engineering fields, machine learning applications, and other practical optimization scenarios. These results indicate that CDO is a promising tool for tackling highly complex and multidimensional problems in academic and industrial environments.