Cloud Drift Optimization (CDO)

Cloud Drift Optimization (CDO) Algorithm – A Nature-Inspired Metaheuristic

Cloud Drift Optimization (CDO) is a novel metaheuristic algorithm inspired by the drifting behavior of cloud particles under atmospheric forces. It introduces adaptive weighting and nonlinear drift mechanisms that improve the balance between exploration and exploitation during the search process.

Unlike conventional algorithms such as PSO, HHO, GWO, and MPA, the CDO algorithm offers:

• Adaptive weight adjustment for dynamic control of the search process.
• Probabilistic drift strategy for escaping local optima.
• Fast convergence with high robustness and solution accuracy.
• Validated performance using Wilcoxon and Friedman statistical tests.

CDO has been benchmarked on unimodal and multimodal functions, as well as real-world engineering problems (cantilever beams, trusses, springs, pressure vessels). Results show that CDO consistently provides lightweight and cost-efficient solutions while meeting design constraints.

This implementation can be applied to engineering design, structural optimization, and machine learning tasks, making it a versatile tool for both academic and industrial applications.

📌 For theoretical background, statistical results, and extended discussion, please refer to the original publication:

https://link.springer.com/article/10.1007/s10791-025-09671-6

(Software reference)

Alibabaei Shahraki, M. (2025). Cloud Drift Optimization (CDO): A MATLAB Implementation. SSRN. https://doi.org/10.1007/s10791-025-09671-6