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A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed Data-Driven Optimization

Title
A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed Data-Driven Optimization
Author
Jun Zhang
Keywords
Computing and Processing; Optimization; Data models; Evolutionary computation; Distributed databases; Classification algorithms; Predictive models; Linear programming; Distributed optimization; data-driven; surrogate-assisted evolutionary algorithm; multisurrogate; classification
Issue Date
2024-03
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex datadriven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate models based on partial data, local models are usually not accurate and even conflicting. To address these challenges, this paper proposes a classifier-ensemble-based surrogate-assisted evolutionary algorithm (CESAEA) with the following features. First, the local nodes in CESAEA train classifiers as surrogate models based on their own data to classify candidates into several levels according to their fitness quality. The classifiers are less sensitive to the partial and biased data than regression models in local nodes. Second, the central node in CESAEA ensembles the local surrogates to form a global classifier with a relaxation condition to guide the evolutionary optimizer to generate promising candidates. The relaxation condition helps to overcome the problem of local model inconsistency. Overall, CESAEA is composed of local classifier construction, global classifier ensemble, classifier-assisted evolutionary optimization and local regression-assisted selection. As only classifiers are allowed to transmit from local nodes to the central node, the mapping relationship between decision vector and objective is hidden and thus data privacy is protected. The experimental results on benchmark functions as well as distributed feature selection problems verify the effectiveness of CESAEA compared to several state-of-the-art approaches.
URI
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418547https://repository.hanyang.ac.kr/handle/20.500.11754/189529
ISSN
1089-778X; 1941-0026
DOI
10.1109/TEVC.2024.3361000
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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