Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment
Recent trends indicate the rapid growth of nature-inspired techniques in the field of optimization. Salp Swarm Algorithm (SSA) is a recently introduced stochastic algorithm that is inspired by the navigational capability and foraging behavior of Salps. However, classical SSA gives unsatisfactory res...
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| Format: | Article |
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Springer
2022-04-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819301144 |
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| author | Mudassar Ali Syed Raziuddin Syed |
| author_facet | Mudassar Ali Syed Raziuddin Syed |
| author_sort | Mudassar Ali Syed |
| collection | DOAJ |
| description | Recent trends indicate the rapid growth of nature-inspired techniques in the field of optimization. Salp Swarm Algorithm (SSA) is a recently introduced stochastic algorithm that is inspired by the navigational capability and foraging behavior of Salps. However, classical SSA gives unsatisfactory results on higher dimension problems depicting poor convergence rate. The search process of SSA lacks exploration and exploitation resulting in convergence inefficiency. This paper proposes a strategy based on the weighted distance position update called Weighted Salp Swarm Algorithm (WSSA) to enhance the performance and convergence rate of the SSA. The proposed WSSA is validated using different benchmark functions and analyzed against seven different stochastic algorithms. The validation results confirmed enhanced performance and convergence rate of WSSA. Moreover, the proposed variant is applied for optimal sensor deployment task. WSSA approach is applied on probabilistic sensor model to maximize coverage and radio energy model to minimize energy consumption. This strategy is a trade-off between coverage and energy efficiency of the sensor network. It was observed that WSSA algorithm outperformed all the other stochastic algorithms in optimizing coverage and energy efficiency of Wireless Sensor Network (WSN). |
| format | Article |
| id | doaj-art-dd3ab145de7a4526ab3f18ff80c8d2b6 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-dd3ab145de7a4526ab3f18ff80c8d2b62025-08-20T03:49:08ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-04-013441285129510.1016/j.jksuci.2019.07.005Weighted Salp Swarm Algorithm and its applications towards optimal sensor deploymentMudassar Ali Syed0Raziuddin Syed1Dept. of Computer Science and Engineering, Deccan College of Engineering and Technology, Hyderabad, IndiaCorresponding author.; Dept. of Computer Science and Engineering, Deccan College of Engineering and Technology, Hyderabad, IndiaRecent trends indicate the rapid growth of nature-inspired techniques in the field of optimization. Salp Swarm Algorithm (SSA) is a recently introduced stochastic algorithm that is inspired by the navigational capability and foraging behavior of Salps. However, classical SSA gives unsatisfactory results on higher dimension problems depicting poor convergence rate. The search process of SSA lacks exploration and exploitation resulting in convergence inefficiency. This paper proposes a strategy based on the weighted distance position update called Weighted Salp Swarm Algorithm (WSSA) to enhance the performance and convergence rate of the SSA. The proposed WSSA is validated using different benchmark functions and analyzed against seven different stochastic algorithms. The validation results confirmed enhanced performance and convergence rate of WSSA. Moreover, the proposed variant is applied for optimal sensor deployment task. WSSA approach is applied on probabilistic sensor model to maximize coverage and radio energy model to minimize energy consumption. This strategy is a trade-off between coverage and energy efficiency of the sensor network. It was observed that WSSA algorithm outperformed all the other stochastic algorithms in optimizing coverage and energy efficiency of Wireless Sensor Network (WSN).http://www.sciencedirect.com/science/article/pii/S1319157819301144Wireless Sensor NetworkOptimizationCoverageEnergy efficiencyStochastic algorithm |
| spellingShingle | Mudassar Ali Syed Raziuddin Syed Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment Journal of King Saud University: Computer and Information Sciences Wireless Sensor Network Optimization Coverage Energy efficiency Stochastic algorithm |
| title | Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment |
| title_full | Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment |
| title_fullStr | Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment |
| title_full_unstemmed | Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment |
| title_short | Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment |
| title_sort | weighted salp swarm algorithm and its applications towards optimal sensor deployment |
| topic | Wireless Sensor Network Optimization Coverage Energy efficiency Stochastic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819301144 |
| work_keys_str_mv | AT mudassaralisyed weightedsalpswarmalgorithmanditsapplicationstowardsoptimalsensordeployment AT raziuddinsyed weightedsalpswarmalgorithmanditsapplicationstowardsoptimalsensordeployment |