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Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem

The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating s...

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Autores principales: Pan, Jeng-Shyang, Zhang, Si-Qi, Chu, Shu-Chuan, Yang, Hong-Mei, Yan, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858194/
https://www.ncbi.nlm.nih.gov/pubmed/36673312
http://dx.doi.org/10.3390/e25010171
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author Pan, Jeng-Shyang
Zhang, Si-Qi
Chu, Shu-Chuan
Yang, Hong-Mei
Yan, Bin
author_facet Pan, Jeng-Shyang
Zhang, Si-Qi
Chu, Shu-Chuan
Yang, Hong-Mei
Yan, Bin
author_sort Pan, Jeng-Shyang
collection PubMed
description The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm.
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spelling pubmed-98581942023-01-21 Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem Pan, Jeng-Shyang Zhang, Si-Qi Chu, Shu-Chuan Yang, Hong-Mei Yan, Bin Entropy (Basel) Article The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm. MDPI 2023-01-14 /pmc/articles/PMC9858194/ /pubmed/36673312 http://dx.doi.org/10.3390/e25010171 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pan, Jeng-Shyang
Zhang, Si-Qi
Chu, Shu-Chuan
Yang, Hong-Mei
Yan, Bin
Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title_full Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title_fullStr Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title_full_unstemmed Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title_short Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
title_sort willow catkin optimization algorithm applied in the tdoa-fdoa joint location problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858194/
https://www.ncbi.nlm.nih.gov/pubmed/36673312
http://dx.doi.org/10.3390/e25010171
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