A multi-objective adaptive surrogate modelling-based optimization algorithm for constrained hybrid problems-Reference-Cited by-同舟云学术

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A multi-objective adaptive surrogate modelling-based optimization algorithm for constrained hybrid problems

Published:2022-02 Issue: Volume:148 Page:105272
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Sun Ruochen,Duan Qingyun,Mao Xiyezi

Funder

Chinese Academy of Sciences

Beijing Normal University

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Ecological Modeling,Environmental Engineering,Software

Reference55 articles.

1. A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices;Ahmadi;Water Resour. Res.,2013

2. Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection;Akhtar;J. Global Optim.,2016

3. Constrained efficient global optimization with support vector machines;Basudhar;Struct. Multidiscip. Optim.,2012

4. A surrogate-based evolutionary algorithm for highly constrained design problems;Beauthier,2017

5. Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks;Behzadian;Environ. Model. Software,2009

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