Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things
Abstract In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Tradit...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99429-3 |
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| author | Shailendra Pratap Singh Gyanendra Kumar Umakant Ahirwar Shitharth Selvarajan Firoz Khan |
| author_facet | Shailendra Pratap Singh Gyanendra Kumar Umakant Ahirwar Shitharth Selvarajan Firoz Khan |
| author_sort | Shailendra Pratap Singh |
| collection | DOAJ |
| description | Abstract In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges. |
| format | Article |
| id | doaj-art-c75748d39ad046dbbe4f30c20dbf25e8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c75748d39ad046dbbe4f30c20dbf25e82025-08-20T02:55:29ZengNature PortfolioScientific Reports2045-23222025-04-0115112710.1038/s41598-025-99429-3Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of thingsShailendra Pratap Singh0Gyanendra Kumar1Umakant Ahirwar2Shitharth Selvarajan3Firoz Khan4Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology Gorakhpur-273010 (U.P.)Department of IoT and IS, Manipal University JaipurComputer Engineering and Application, GLA UniversityDepartment of Computer Science, Kebri Dehar UniversityCenter for Information and Communication Sciences, Ball State UniversityAbstract In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.https://doi.org/10.1038/s41598-025-99429-3 |
| spellingShingle | Shailendra Pratap Singh Gyanendra Kumar Umakant Ahirwar Shitharth Selvarajan Firoz Khan Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things Scientific Reports |
| title | Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things |
| title_full | Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things |
| title_fullStr | Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things |
| title_full_unstemmed | Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things |
| title_short | Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things |
| title_sort | multi objective quantum hybrid evolutionary algorithms for enhancing quality of service in internet of things |
| url | https://doi.org/10.1038/s41598-025-99429-3 |
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