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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
J. Lin, C. He, Y. Tian, and L. Pan, “Variable reconstruction for evolutionary expensive large-scale multiobjective optimization and its application on aerodynamic design,” IEEE/CAA J. Autom. Sinica, 2024.
Citation: J. Lin, C. He, Y. Tian, and L. Pan, “Variable reconstruction for evolutionary expensive large-scale multiobjective optimization and its application on aerodynamic design,” IEEE/CAA J. Autom. Sinica, 2024.
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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

Funds:  The work was supported by the National Natural Science Foundation of China (U20A20306, 62276191) and the Fundamental Research Funds for the Central Universities (HUST2023JYCXJJ011)
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    Abstract
  • Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

     

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