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On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget
Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but almost completely disjoint communities (they have different journals, different conferences, different te...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765181/ https://www.ncbi.nlm.nih.gov/pubmed/29323223 http://dx.doi.org/10.1038/s41598-017-18940-4 |
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author | Sergeyev, Ya. D. Kvasov, D. E. Mukhametzhanov, M. S. |
author_facet | Sergeyev, Ya. D. Kvasov, D. E. Mukhametzhanov, M. S. |
author_sort | Sergeyev, Ya. D. |
collection | PubMed |
description | Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but almost completely disjoint communities (they have different journals, different conferences, different test functions, etc.) solving these problems: a broad community of practitioners using stochastic nature-inspired metaheuristics and people from academia studying deterministic mathematical programming methods. In order to bridge the gap between these communities we propose a visual technique for a systematic comparison of global optimization algorithms having different nature. Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations. |
format | Online Article Text |
id | pubmed-5765181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57651812018-01-17 On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget Sergeyev, Ya. D. Kvasov, D. E. Mukhametzhanov, M. S. Sci Rep Article Global optimization problems where evaluation of the objective function is an expensive operation arise frequently in engineering, decision making, optimal control, etc. There exist two huge but almost completely disjoint communities (they have different journals, different conferences, different test functions, etc.) solving these problems: a broad community of practitioners using stochastic nature-inspired metaheuristics and people from academia studying deterministic mathematical programming methods. In order to bridge the gap between these communities we propose a visual technique for a systematic comparison of global optimization algorithms having different nature. Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations. Nature Publishing Group UK 2018-01-11 /pmc/articles/PMC5765181/ /pubmed/29323223 http://dx.doi.org/10.1038/s41598-017-18940-4 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sergeyev, Ya. D. Kvasov, D. E. Mukhametzhanov, M. S. On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title | On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title_full | On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title_fullStr | On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title_full_unstemmed | On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title_short | On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
title_sort | on the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765181/ https://www.ncbi.nlm.nih.gov/pubmed/29323223 http://dx.doi.org/10.1038/s41598-017-18940-4 |
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