Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study
In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB<sup>®</sup>) and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/1894 |
| _version_ | 1797473716425195520 |
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| author | João Fé Sérgio D. Correia Slavisa Tomic Marko Beko |
| author_facet | João Fé Sérgio D. Correia Slavisa Tomic Marko Beko |
| author_sort | João Fé |
| collection | DOAJ |
| description | In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB<sup>®</sup>) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms. |
| first_indexed | 2024-03-09T20:21:22Z |
| format | Article |
| id | doaj.art-6a6b8a8a83f44720aafae7d7e5035242 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:22Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6a6b8a8a83f44720aafae7d7e50352422023-11-23T23:47:52ZengMDPI AGSensors1424-82202022-02-01225189410.3390/s22051894Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive StudyJoão Fé0Sérgio D. Correia1Slavisa Tomic2Marko Beko3COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalCOPELABS, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalCOPELABS, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalInstituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, PortugalIn the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB<sup>®</sup>) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.https://www.mdpi.com/1424-8220/22/5/1894swarm optimizationacoustic localizationembedded programmingwireless sensor networkmetaheuristicedge computing |
| spellingShingle | João Fé Sérgio D. Correia Slavisa Tomic Marko Beko Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study Sensors swarm optimization acoustic localization embedded programming wireless sensor network metaheuristic edge computing |
| title | Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study |
| title_full | Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study |
| title_fullStr | Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study |
| title_full_unstemmed | Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study |
| title_short | Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study |
| title_sort | swarm optimization for energy based acoustic source localization a comprehensive study |
| topic | swarm optimization acoustic localization embedded programming wireless sensor network metaheuristic edge computing |
| url | https://www.mdpi.com/1424-8220/22/5/1894 |
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