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Political optimizer with interpolation strategy for global optimization
Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101970/ https://www.ncbi.nlm.nih.gov/pubmed/33956841 http://dx.doi.org/10.1371/journal.pone.0251204 |
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author | Zhu, Aijun Gu, Zhanqi Hu, Cong Niu, Junhao Xu, Chuanpei Li, Zhi |
author_facet | Zhu, Aijun Gu, Zhanqi Hu, Cong Niu, Junhao Xu, Chuanpei Li, Zhi |
author_sort | Zhu, Aijun |
collection | PubMed |
description | Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity. |
format | Online Article Text |
id | pubmed-8101970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81019702021-05-17 Political optimizer with interpolation strategy for global optimization Zhu, Aijun Gu, Zhanqi Hu, Cong Niu, Junhao Xu, Chuanpei Li, Zhi PLoS One Research Article Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity. Public Library of Science 2021-05-06 /pmc/articles/PMC8101970/ /pubmed/33956841 http://dx.doi.org/10.1371/journal.pone.0251204 Text en © 2021 Zhu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhu, Aijun Gu, Zhanqi Hu, Cong Niu, Junhao Xu, Chuanpei Li, Zhi Political optimizer with interpolation strategy for global optimization |
title | Political optimizer with interpolation strategy for global optimization |
title_full | Political optimizer with interpolation strategy for global optimization |
title_fullStr | Political optimizer with interpolation strategy for global optimization |
title_full_unstemmed | Political optimizer with interpolation strategy for global optimization |
title_short | Political optimizer with interpolation strategy for global optimization |
title_sort | political optimizer with interpolation strategy for global optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101970/ https://www.ncbi.nlm.nih.gov/pubmed/33956841 http://dx.doi.org/10.1371/journal.pone.0251204 |
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