HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem
Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/11/12/675 |
| _version_ | 1797461465343459328 |
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| author | Mengjian Zhang Deguang Wang Ming Yang Wei Tan Jing Yang |
| author_facet | Mengjian Zhang Deguang Wang Ming Yang Wei Tan Jing Yang |
| author_sort | Mengjian Zhang |
| collection | DOAJ |
| description | Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle swarm butterfly algorithm (HPSBA), by combining their strengths of particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), for solving this problem. Significantly, the value of individual scent intensity should be non-negative without consideration of the basic BOA, which is calculated with absolute value of the proposed HPSBA. Moreover, the performance of the HPSBA is comprehensively compared with the fundamental BOA, numerous potential BOA variants, and tried-and-true algorithms, for solving the twenty-six commonly used benchmark functions. The results show that HPSBA has a competitive overall performance. Finally, when compared to PSO, BOA, and MBOA, HPSBA is used to solve the node coverage optimization problem in WSN. The experimental results demonstrate that the HPSBA optimized coverage has a higher coverage rate, which effectively reduces node redundancy and extends WSN survival time. |
| first_indexed | 2024-03-09T17:19:47Z |
| format | Article |
| id | doaj.art-0b6d45d3a2374ab1b0b71c6e6f993e2b |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-09T17:19:47Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-0b6d45d3a2374ab1b0b71c6e6f993e2b2023-11-24T13:15:12ZengMDPI AGAxioms2075-16802022-11-01111267510.3390/axioms11120675HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization ProblemMengjian Zhang0Deguang Wang1Ming Yang2Wei Tan3Jing Yang4Electrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaCollege of Forestry, Guizhou University, Guiyang 550025, ChinaElectrical Engineering College, Guizhou University, Guiyang 550025, ChinaComplex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle swarm butterfly algorithm (HPSBA), by combining their strengths of particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), for solving this problem. Significantly, the value of individual scent intensity should be non-negative without consideration of the basic BOA, which is calculated with absolute value of the proposed HPSBA. Moreover, the performance of the HPSBA is comprehensively compared with the fundamental BOA, numerous potential BOA variants, and tried-and-true algorithms, for solving the twenty-six commonly used benchmark functions. The results show that HPSBA has a competitive overall performance. Finally, when compared to PSO, BOA, and MBOA, HPSBA is used to solve the node coverage optimization problem in WSN. The experimental results demonstrate that the HPSBA optimized coverage has a higher coverage rate, which effectively reduces node redundancy and extends WSN survival time.https://www.mdpi.com/2075-1680/11/12/675particle swarm optimizationbutterfly optimization algorithmhybrid algorithmconvergence analysisWireless Sensor Networknode coverage |
| spellingShingle | Mengjian Zhang Deguang Wang Ming Yang Wei Tan Jing Yang HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem Axioms particle swarm optimization butterfly optimization algorithm hybrid algorithm convergence analysis Wireless Sensor Network node coverage |
| title | HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem |
| title_full | HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem |
| title_fullStr | HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem |
| title_full_unstemmed | HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem |
| title_short | HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem |
| title_sort | hpsba a modified hybrid framework with convergence analysis for solving wireless sensor network coverage optimization problem |
| topic | particle swarm optimization butterfly optimization algorithm hybrid algorithm convergence analysis Wireless Sensor Network node coverage |
| url | https://www.mdpi.com/2075-1680/11/12/675 |
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