An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem

Abstract

The utilization of metaheuristics for optimizing wind farm layouts (WFLOP) has emerged as a popular research area in recent years. However, effectively screening and improving metaheuristics to obtain optimal layouts remain a challenging task. Traditional metaheuristic screening methods require testing numerous algorithms, resulting in high computational resource consumption and trial-and-error costs due to the lack of theoretical guidance. To overcome this challenge, this study proposes a complex network-based metaheuristic screening method. Population interaction networks are utilized to classify metaheuristics into two categories: biased exploitation and biased exploration. The results of several metaheuristics on WFLOP suggest that exploration-biased algorithms generally outperform exploitation-biased ones. This discovery holds great significance as it has the potential to predict the performance of various algorithms on WFLOP to a certain degree. Additionally, it provides valuable suggestions for algorithm selection and improvement. Building upon this new methodology, we screen and improve the spherical evolution algorithm to enhance its exploration capabilities. Experimental results demonstrate that the improved spherical evolution algorithm significantly outperforms its competitors on WFLOP.