ELMOEA/D-DE: Extreme Learning Surrogate Models in Multi-objective Optimization Based on Decomposition and Differential Evolution-Reference-Cited by-同舟云学术

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ELMOEA/D-DE: Extreme Learning Surrogate Models in Multi-objective Optimization Based on Decomposition and Differential Evolution

Published:2014-10 Issue: Volume: Page:
ISSN:
Container-title:2014 Brazilian Conference on Intelligent Systems
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Author:

Pavelski Lucas M.,Delgado Myriam R.,Almeida Carolina P. de,Goncalves Richard A.,Venske Sandra M.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/6979382/6984785/06984850.pdf?arnumber=6984850

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey;Evolutionary Intelligence;2023-10-03

2. Extreme Learning Machine-based Genetic Algorithm for the facility location problem with distributed demands on network edges;2023 IEEE Congress on Evolutionary Computation (CEC);2023-07-01

3. An enhanced decomposition-based multi-objective evolutionary algorithm with a self-organizing collaborative scheme;Expert Systems with Applications;2023-03

4. An Enhanced Decomposition-Based Multi-Objective Evolutionary Algorithm with a Self-Organizing Collaborative Scheme;SSRN Electronic Journal;2022

5. Adaptation of Search Generations in Extreme Learning Assisted MOEA/D Based on Estimation Accuracy of Surrogate Model;2021 IEEE Congress on Evolutionary Computation (CEC);2021-06-28

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