Multi-objective evolutionary optimization of unsupervised latent variables of turning process-Reference-Cited by-同舟云学术

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Multi-objective evolutionary optimization of unsupervised latent variables of turning process

Published:2022-05 Issue: Volume:120 Page:108713
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

de Melo Simone Aparecida,Pereira Robson Bruno Dutra^ORCID,da Silva Reis Allexandre Fortes^ORCID,Lauro Carlos Henrique^ORCID,Brandão Lincoln Cardoso

Publisher

Elsevier BV

Subject

Software

Reference54 articles.

1. An indicator and adaptive region division based evolutionary algorithm for many-objective optimization;Zhou;Appl. Soft Comput.,2021

2. Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data;Peralta;Appl. Soft Comput.,2020

3. Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?;Oliveira;Knowl.-Based Syst.,2020

4. Multivariate GR&R through factor analysis;Aparecida Mendonça Marques;Measurement,2020

5. A fast and elitist multiobjective genetic algorithm: NSGA-II;Deb;Trans. Evol. Comp.,2002

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1. Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm;Processes;2024-05-07

2. Multi-objective evolutionary optimization of extreme gradient boosting regression models of the internal turning of PEEK tubes;Expert Systems with Applications;2024-03

3. Multi-objective optimization of electrical discharge machining parameters using particle swarm optimization;Applied Soft Computing;2024-03

4. Analysis of CNC turning parameters and simultaneous optimisation of surface roughness and material removal rate by MOGA for AISI 4340 alloy steel;Advances in Materials and Processing Technologies;2023-11-12

5. A multi-objective robust evolutionary optimization approach applied to the multivariate helical milling process of super duplex steel;Applied Soft Computing;2023-11

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