首页|Enhancing SAEAs with unevaluated solutions:a case study of relation model for expensive optimization

Enhancing SAEAs with unevaluated solutions:a case study of relation model for expensive optimization

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Surrogate-assisted evolutionary algorithms(SAEAs)hold significant importance in resolving ex-pensive optimization problems.Extensive efforts have been devoted to improving the efficiency of SAEAs through the development of proficient model-assisted solution selection methods,which,however,first re-quires generating high-quality solutions.The fundamental paradigm of evaluating a limited number of so-lutions in each generation within SAEAs reduces the variance of adjacent populations,thereby affecting the quality of offspring solutions.This is a frequently encountered issue yet receives little attention.To address the issue,this paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs.The surrogate model is employed to identify high-quality solutions for the direct generation of new solutions without evaluation.To ensure dependable selection,we have introduced two tailored relation models to select the optimal solution and the unevaluated population.A comprehensive experimental analysis is performed on two test suites,which showcases the superiority of the relation model over regression and classification models in the solution selection phase.Furthermore,the surrogate-selected unevaluated solutions with high potential have been shown to enhance the efficiency of the algorithm significantly.

expensive optimizationunevaluated solutionsrelation modelsurrogate-assisted evolutionary algorithm

Hao HAO、Xiaoqun ZHANG、Aimin ZHOU

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Institute of Natural Sciences,Shanghai Jiao Tong University,Shanghai 200240,China

Shanghai Frontiers Science Center of Molecule Intelligent Syntheses,Shanghai 200062,China

School of Computer Science and Technology,East China Normal University,Shanghai 200062,China

National Natural Science Foundation of ChinaChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationNational Supported Postdoctoral Research Program

623061742023M742252023TQ0213GZC20231588

2024

10.1007/s11432-023-3909-x

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(2)
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