Cargando…

An Improved CMA-ES for Solving Large Scale Optimization Problem

In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous genera...

Descripción completa

Detalles Bibliográficos
Autores principales: Jin, Jin, Yang, Chuan, Zhang, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354820/
http://dx.doi.org/10.1007/978-3-030-53956-6_34
_version_ 1783558172100788224
author Jin, Jin
Yang, Chuan
Zhang, Yi
author_facet Jin, Jin
Yang, Chuan
Zhang, Yi
author_sort Jin, Jin
collection PubMed
description In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems.
format Online
Article
Text
id pubmed-7354820
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-73548202020-07-13 An Improved CMA-ES for Solving Large Scale Optimization Problem Jin, Jin Yang, Chuan Zhang, Yi Advances in Swarm Intelligence Article In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems. 2020-06-22 /pmc/articles/PMC7354820/ http://dx.doi.org/10.1007/978-3-030-53956-6_34 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Jin, Jin
Yang, Chuan
Zhang, Yi
An Improved CMA-ES for Solving Large Scale Optimization Problem
title An Improved CMA-ES for Solving Large Scale Optimization Problem
title_full An Improved CMA-ES for Solving Large Scale Optimization Problem
title_fullStr An Improved CMA-ES for Solving Large Scale Optimization Problem
title_full_unstemmed An Improved CMA-ES for Solving Large Scale Optimization Problem
title_short An Improved CMA-ES for Solving Large Scale Optimization Problem
title_sort improved cma-es for solving large scale optimization problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354820/
http://dx.doi.org/10.1007/978-3-030-53956-6_34
work_keys_str_mv AT jinjin animprovedcmaesforsolvinglargescaleoptimizationproblem
AT yangchuan animprovedcmaesforsolvinglargescaleoptimizationproblem
AT zhangyi animprovedcmaesforsolvinglargescaleoptimizationproblem
AT jinjin improvedcmaesforsolvinglargescaleoptimizationproblem
AT yangchuan improvedcmaesforsolvinglargescaleoptimizationproblem
AT zhangyi improvedcmaesforsolvinglargescaleoptimizationproblem