An effective surrogate model assisted algorithm for multi-objective optimization: application to wind farm layout design

Frontiers in Energy Research

Energy Research

Yong Chen¹  Li Wang²  Hui Huang³ 

[1] Innovation and Entrepreneurship College, Changsha Normal University, Changsha, China;School of Automation, Central South University, Changsha, China;School of Physical Education, Huanan Normal University, Changsha, China;

关键词: multi-objective optimization;    sparse Gaussian process;    surrogate model;    adaptive grid multi-objective particle swarm optimization algorithm;    wind power engineering;   

DOI  :  10.3389/fenrg.2023.1239332

 received in 2023-06-13, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Wang and Huang.