Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 71-80.DOI: 10.3778/j.issn.1002-8331.2012-0010

• Theory, Research and Development • Previous Articles     Next Articles

Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm

CHEN Wanfen, WANG Yujia, LIN Weixing   

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • Online:2021-12-01 Published:2021-12-02



  1. 上海工程技术大学 电子电气工程学院,上海 201620


Aiming at the problem that the surrogate-assisted evolutionary algorithm is difficult to construct a high-quality surrogate model from a small number of sample points when reducing expensive fitness evaluation, a heterogeneous ensemble surrogate-assisted multi-objective particle swarm optimization algorithm is proposed. This method uses the weighted average method to combine the Kriging model and the radial basis function network model into a high-precision heterogeneous integrated model to achieve the purpose of enhancing the algorithm’s ability to process uncertain information. Two surrogate models based on ensemble learning are applied to global search and local search respectively. Based on the framework of multi-objective particle swarm optimization algorithm, the newly proposed method adaptively constructs a heterogeneous ensemble model for each objective function, and then uses the non-dominated solution of the model to guide the update of the particle swarm and obtains the optimal solution set of the objective function. Experimental results show that the proposed method improves the search capability of the surrogate model, reduces the number of evaluations, and as the search dimension increases, its computational complexity also has better scalability.

Key words: multi-objective optimization, particle swarm optimization algorithm, Kriging model, radial basis function network model, heterogeneous ensemble



关键词: 多目标优化, 粒子群优化算法, Kriging模型, 径向基函数网络模型, 异构集成