Quantum-inspired adaptive mutation operator enabled PSO (QAMO-PSO) for parallel optimization and tailoring parameters of Kolmogorov–Arnold network
Particle swarm optimizer (PSO) is a biomimetic optimization algorithm well-known for its potential in addressing diversified optimization problems (OP). Although PSO is based on swarm-cognitive, it often suffers from attaining the global optima and the balance between exploration and exploitation, l...
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| Veröffentlicht in: | The Journal of supercomputing Jg. 81; H. 14; S. 1310 |
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| Sprache: | Englisch |
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06.09.2025
Springer Nature B.V |
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| ISSN: | 1573-0484, 0920-8542, 1573-0484 |
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| Abstract | Particle swarm optimizer (PSO) is a biomimetic optimization algorithm well-known for its potential in addressing diversified optimization problems (OP). Although PSO is based on swarm-cognitive, it often suffers from attaining the global optima and the balance between exploration and exploitation, leading to untimely convergence and compromised outcomes. To enhance such shortcomings, we have proposed the quantum adaptive mutation operator PSO (QAMO-PSO), where the QAMO is integrated with the standard PSO. In the suggested approach, the qubit dynamics and adaptable mutation concept are employed to amplify the search region of swarms, while preserving superior outcomes by altering the locations of swarms concerning the quanta rotation and including a dynamic quantum-inspired mutation mechanism. The QAMO frameworks concurrently modify the mutation likelihood regarding the global best fitness, adhering it to randomness to suppress the confined optima alongside accelerating optimal convergence. The competency of QAMO-PSO is rigorously quantified over the IEEE-CEC-2022 benchmark problem suite. The anticipated QAMO-PSO reveals commendable findings over the shifted and rotated (SR), hybrid and composition functions pertaining to CEC-2022. Furthermore, a cascade of statistical analysis is conducted to substantiate the distinctiveness of QAMO-PSO. Additionally, the applicability of QAMO-PSO is assessed in real-life constraints by tuning the hyperparameters of Kolmogorov–Arnold network (KAN), showcasing its efficacy in multivariate and nonlinear feature space. The QAMO-PSO as a trainer algorithm for KAN reveals prevailing results over the other baseline algorithms. Finally, the QAMO-PSO is a viable and reliable hybrid algorithm (HA) to tackle optimization problems related to engineering and computational cognition. |
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| AbstractList | Particle swarm optimizer (PSO) is a biomimetic optimization algorithm well-known for its potential in addressing diversified optimization problems (OP). Although PSO is based on swarm-cognitive, it often suffers from attaining the global optima and the balance between exploration and exploitation, leading to untimely convergence and compromised outcomes. To enhance such shortcomings, we have proposed the quantum adaptive mutation operator PSO (QAMO-PSO), where the QAMO is integrated with the standard PSO. In the suggested approach, the qubit dynamics and adaptable mutation concept are employed to amplify the search region of swarms, while preserving superior outcomes by altering the locations of swarms concerning the quanta rotation and including a dynamic quantum-inspired mutation mechanism. The QAMO frameworks concurrently modify the mutation likelihood regarding the global best fitness, adhering it to randomness to suppress the confined optima alongside accelerating optimal convergence. The competency of QAMO-PSO is rigorously quantified over the IEEE-CEC-2022 benchmark problem suite. The anticipated QAMO-PSO reveals commendable findings over the shifted and rotated (SR), hybrid and composition functions pertaining to CEC-2022. Furthermore, a cascade of statistical analysis is conducted to substantiate the distinctiveness of QAMO-PSO. Additionally, the applicability of QAMO-PSO is assessed in real-life constraints by tuning the hyperparameters of Kolmogorov–Arnold network (KAN), showcasing its efficacy in multivariate and nonlinear feature space. The QAMO-PSO as a trainer algorithm for KAN reveals prevailing results over the other baseline algorithms. Finally, the QAMO-PSO is a viable and reliable hybrid algorithm (HA) to tackle optimization problems related to engineering and computational cognition. |
| ArticleNumber | 1310 |
| Author | Agrawal, Umang Kumar Panda, Nibedan |
| Author_xml | – sequence: 1 givenname: Umang Kumar surname: Agrawal fullname: Agrawal, Umang Kumar organization: School of Computer Engineering, KIIT Deemed to Be University – sequence: 2 givenname: Nibedan surname: Panda fullname: Panda, Nibedan email: nibedan.pandafcs@kiit.ac.in organization: School of Computer Engineering, KIIT Deemed to Be University |
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| Cites_doi | 10.1007/s10489-024-06096-4 10.1016/j.engappai.2024.109987 10.1111/coin.12272 10.1016/j.future.2020.03.055 10.1016/j.aei.2024.102464 10.1007/s11227-024-06507-w 10.1007/s00422-025-01004-6 10.1016/j.engappai.2021.104314 10.1016/j.procs.2025.04.663 10.1016/j.future.2024.04.008 10.1016/j.cma.2024.117699 10.1007/s11071-024-10245-2 10.1007/s10586-023-03993-0 10.1016/j.eswa.2023.121270 10.1109/ICNN.1995.488968 10.1007/978-981-96-0047-2_19 10.1038/s41598-022-18351-0 10.1002/advs.202413805 10.1109/ACCESS.2023.3286347 10.1007/978-981-15-6353-9_8 10.1038/s41598-025-90040-0 10.1038/s41598-024-85083-8 10.1007/s11227-025-07324-5 10.1038/s41598-024-69360-0 10.1007/s13369-019-04132-x 10.1007/s10586-024-04833-5 10.1016/j.jmsy.2024.02.007 10.1016/j.compbiomed.2024.108064 10.1016/j.cma.2020.113609 10.1016/j.neucom.2025.129414 10.1186/s40537-025-01116-7 10.1016/j.eswa.2024.125496 10.1007/s10479-023-05228-2 10.1016/j.apenergy.2024.124748 10.1016/j.egyr.2024.12.038 10.1016/j.swevo.2024.101836 10.1007/s13369-022-07408-x 10.1016/j.knosys.2021.106859 10.1007/s10586-024-04750-7 10.1007/s10462-024-11023-7 10.1016/j.eswa.2025.126406 10.1016/j.cma.2022.114616 10.1016/j.procs.2025.04.064 10.1109/4235.585893 10.1109/ACCESS.2021.3115026 10.1038/s41598-025-88054-9 10.1016/j.csite.2025.105815 10.1007/s11227-024-06022-y 10.1016/j.ins.2018.08.030 10.1007/s11042-020-10304-x 10.1016/j.asoc.2021.107122 10.1016/j.neucom.2024.128427 10.1016/j.eswa.2021.115353 10.1016/j.knosys.2019.105190 10.1007/s11063-022-10850-5 10.1016/j.oceaneng.2025.120704 10.1007/s13369-024-09113-3 |
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| References_xml | – volume: 55 start-page: 233 issue: 3 year: 2025 ident: 7810_CR4 publication-title: Appl Intell doi: 10.1007/s10489-024-06096-4 – volume: 143 year: 2025 ident: 7810_CR9 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2024.109987 – volume: 36 start-page: 320 issue: 1 year: 2020 ident: 7810_CR18 publication-title: Comput Intell doi: 10.1111/coin.12272 – volume: 111 start-page: 300 year: 2020 ident: 7810_CR49 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2020.03.055 – volume: 61 year: 2024 ident: 7810_CR48 publication-title: Adv Eng Inform doi: 10.1016/j.aei.2024.102464 – volume: 81 start-page: 1 issue: 1 year: 2025 ident: 7810_CR1 publication-title: J Supercomput doi: 10.1007/s11227-024-06507-w – volume: 119 start-page: 5 issue: 1 year: 2025 ident: 7810_CR3 publication-title: Biol Cybern doi: 10.1007/s00422-025-01004-6 – volume: 104 year: 2021 ident: 7810_CR45 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2021.104314 – ident: 7810_CR19 – volume: 258 start-page: 4128 year: 2025 ident: 7810_CR10 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2025.04.663 – volume: 157 start-page: 445 year: 2024 ident: 7810_CR24 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2024.04.008 – volume: 436 year: 2025 ident: 7810_CR59 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2024.117699 – volume: 113 start-page: 629 issue: 1 year: 2025 ident: 7810_CR36 publication-title: Nonlinear Dyn doi: 10.1007/s11071-024-10245-2 – volume: 27 start-page: 997 issue: 1 year: 2024 ident: 7810_CR26 publication-title: Cluster Comput doi: 10.1007/s10586-023-03993-0 – volume: 236 year: 2024 ident: 7810_CR23 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.121270 – ident: 7810_CR8 doi: 10.1109/ICNN.1995.488968 – ident: 7810_CR17 doi: 10.1007/978-981-96-0047-2_19 – volume: 12 start-page: 13977 issue: 1 year: 2022 ident: 7810_CR32 publication-title: Sci Rep doi: 10.1038/s41598-022-18351-0 – year: 2025 ident: 7810_CR56 publication-title: Adv Sci doi: 10.1002/advs.202413805 – volume: 11 start-page: 71143 year: 2023 ident: 7810_CR29 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3286347 – ident: 7810_CR51 doi: 10.1007/978-981-15-6353-9_8 – volume: 15 start-page: 5843 issue: 1 year: 2025 ident: 7810_CR37 publication-title: Sci Rep doi: 10.1038/s41598-025-90040-0 – volume: 15 start-page: 1917 issue: 1 year: 2025 ident: 7810_CR61 publication-title: Sci Rep doi: 10.1038/s41598-024-85083-8 – ident: 7810_CR54 – volume: 81 start-page: 1 issue: 8 year: 2025 ident: 7810_CR16 publication-title: J Supercomput doi: 10.1007/s11227-025-07324-5 – volume: 14 start-page: 18595 issue: 1 year: 2024 ident: 7810_CR25 publication-title: Sci Rep doi: 10.1038/s41598-024-69360-0 – volume: 45 start-page: 2743 issue: 4 year: 2020 ident: 7810_CR20 publication-title: Arab J Sci Eng doi: 10.1007/s13369-019-04132-x – volume: 28 start-page: 155 issue: 3 year: 2025 ident: 7810_CR40 publication-title: Cluster Comput doi: 10.1007/s10586-024-04833-5 – volume: 73 start-page: 334 year: 2024 ident: 7810_CR28 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2024.02.007 – volume: 172 year: 2024 ident: 7810_CR50 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2024.108064 – volume: 376 year: 2021 ident: 7810_CR47 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113609 – volume: 623 year: 2025 ident: 7810_CR58 publication-title: Neurocomputing doi: 10.1016/j.neucom.2025.129414 – volume: 12 start-page: 69 issue: 1 year: 2025 ident: 7810_CR62 publication-title: J Big Data doi: 10.1186/s40537-025-01116-7 – volume: 261 year: 2025 ident: 7810_CR39 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2024.125496 – volume: 346 start-page: 1811 issue: 2 year: 2025 ident: 7810_CR5 publication-title: Ann Oper Res doi: 10.1007/s10479-023-05228-2 – volume: 378 year: 2025 ident: 7810_CR6 publication-title: Appl Energy doi: 10.1016/j.apenergy.2024.124748 – volume: 13 start-page: 713 year: 2025 ident: 7810_CR60 publication-title: Energy Rep doi: 10.1016/j.egyr.2024.12.038 – volume: 93 year: 2025 ident: 7810_CR22 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2024.101836 – volume: 48 start-page: 9991 issue: 8 year: 2023 ident: 7810_CR31 publication-title: Arab J Sci Eng doi: 10.1007/s13369-022-07408-x – volume: 219 year: 2021 ident: 7810_CR35 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2021.106859 – ident: 7810_CR53 – volume: 28 start-page: 91 issue: 2 year: 2025 ident: 7810_CR21 publication-title: Cluster Comput doi: 10.1007/s10586-024-04750-7 – volume: 58 start-page: 84 issue: 3 year: 2025 ident: 7810_CR46 publication-title: Artif Intell Rev doi: 10.1007/s10462-024-11023-7 – volume: 269 year: 2025 ident: 7810_CR11 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2025.126406 – volume: 392 year: 2022 ident: 7810_CR41 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2022.114616 – volume: 259 start-page: 1106 year: 2025 ident: 7810_CR7 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2025.04.064 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 7810_CR12 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.585893 – volume: 9 start-page: 134022 year: 2021 ident: 7810_CR34 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3115026 – volume: 15 start-page: 725 issue: 2 year: 2023 ident: 7810_CR55 publication-title: Int J Inf Technol – volume: 15 start-page: 8648 issue: 1 year: 2025 ident: 7810_CR57 publication-title: Sci Rep doi: 10.1038/s41598-025-88054-9 – year: 2025 ident: 7810_CR13 publication-title: Case Stud Therm Eng doi: 10.1016/j.csite.2025.105815 – volume: 80 start-page: 1 issue: 10 year: 2024 ident: 7810_CR27 publication-title: J Supercomput doi: 10.1007/s11227-024-06022-y – volume: 468 start-page: 117 year: 2018 ident: 7810_CR42 publication-title: Inf Sci doi: 10.1016/j.ins.2018.08.030 – volume: 80 start-page: 35415 issue: 28 year: 2021 ident: 7810_CR38 publication-title: Multimedia Tools Appl doi: 10.1007/s11042-020-10304-x – volume: 13 start-page: 1 issue: 6 year: 2025 ident: 7810_CR14 publication-title: Int J Dyn Control – volume: 102 year: 2021 ident: 7810_CR33 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107122 – volume: 607 year: 2024 ident: 7810_CR43 publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.128427 – volume: 183 year: 2021 ident: 7810_CR52 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2021.115353 – volume: 191 year: 2020 ident: 7810_CR44 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2019.105190 – volume: 55 start-page: 2551 issue: 3 year: 2023 ident: 7810_CR30 publication-title: Neural Process Lett doi: 10.1007/s11063-022-10850-5 – volume: 324 year: 2025 ident: 7810_CR15 publication-title: Ocean Eng doi: 10.1016/j.oceaneng.2025.120704 – volume: 50 start-page: 1025 issue: 2 year: 2025 ident: 7810_CR2 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| Title | Quantum-inspired adaptive mutation operator enabled PSO (QAMO-PSO) for parallel optimization and tailoring parameters of Kolmogorov–Arnold network |
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