Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications

This paper proposes a novel swarm intelligence bioinspired optimization algorithm, called the Dandelion Optimizer (DO), for solving continuous optimization problems. DO simulates the process of dandelion seed long-distance flight relying on wind, which is divided into three stages. In the rising stage, seeds raise in a spiral manner due to the eddies from above or drift locally in communities according to different weather conditions. In the descending stage, flying seeds steadily descend by constantly adjusting their direction in global space. In the landing stage, seeds land in randomly selected positions so that they grow. The moving trajectory of a seed in the descending stage and landing stage are described by Brownian motion and a Levy random walk. CEC2017 benchmark functions are utilized to evaluate the performance of DO, including the optimization accuracy, stability, convergence, and scalability, through a comparison with 9 well-known nature-inspired metaheuristic algorithms. Finally, the applicability of DO is verified by solving 4 real-world optimization problems. The experimental results indicate that the proposed DO method is a higher performing optimizer with outstanding iterative optimization and strong robustness compared with well-established algorithms. Source codes of DO are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/114680-dandelion-optimizer.