Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring
Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/5/393 |
| _version_ | 1797581108913635328 |
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| author | Fei Xia Ming Yang Mengjian Zhang Jing Zhang |
| author_facet | Fei Xia Ming Yang Mengjian Zhang Jing Zhang |
| author_sort | Fei Xia |
| collection | DOAJ |
| description | Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results. |
| first_indexed | 2024-03-10T23:00:32Z |
| format | Article |
| id | doaj.art-a062d22cbe3341fb8339773a3af67d83 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T23:00:32Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-a062d22cbe3341fb8339773a3af67d832023-11-19T09:43:52ZengMDPI AGBiomimetics2313-76732023-08-018539310.3390/biomimetics8050393Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental MonitoringFei Xia0Ming Yang1Mengjian Zhang2Jing Zhang3Electrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaExisting swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.https://www.mdpi.com/2313-7673/8/5/393balanced butterfly optimizerbio-inspired optimizationwireless sensor networknode localizationnode coverageenvironmental monitoring |
| spellingShingle | Fei Xia Ming Yang Mengjian Zhang Jing Zhang Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring Biomimetics balanced butterfly optimizer bio-inspired optimization wireless sensor network node localization node coverage environmental monitoring |
| title | Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring |
| title_full | Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring |
| title_fullStr | Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring |
| title_full_unstemmed | Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring |
| title_short | Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring |
| title_sort | joint light sensitive balanced butterfly optimizer for solving the nlo and nco problems of wsn for environmental monitoring |
| topic | balanced butterfly optimizer bio-inspired optimization wireless sensor network node localization node coverage environmental monitoring |
| url | https://www.mdpi.com/2313-7673/8/5/393 |
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