Reinforcement Learning Enhanced Multi-Objective Social Network Search Algorithm for Engineering Design Problems
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| Publicado en: | Mathematics vol. 13, no. 22 (2025), p. 3613-3641 |
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| Autor principal: | |
| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13223613 |2 doi | |
| 035 | |a 3275541980 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Peng, Wei |u School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China | |
| 245 | 1 | |a Reinforcement Learning Enhanced Multi-Objective Social Network Search Algorithm for Engineering Design Problems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To address real-world engineering design optimization problems, this study proposes a reinforcement learning enhanced multi-objective social network search algorithm (QMOSNS), which represents a novel approach for solving multi-objective optimization problems. QMOSNS utilizes Halton sequences for population initialization to enhance the diversity of the initial population. A multi-objective archive mechanism is implemented to store Pareto-optimal solutions and select parental individuals through a reassigned fitness evaluation strategy. Furthermore, Q-learning is incorporated to adaptively select mutation operators, thereby dynamically balancing the algorithm’s exploration and exploitation capabilities. QMOSNS was rigorously evaluated through 50 prominent case studies, including 22 unconstrained multi-objective benchmark problems, 18 constrained multi-objective benchmark problems, and 10 multi-objective engineering design problems, to comprehensively validate its computational capabilities and effectiveness. Moreover, statistical results obtained using consistent performance metrics were compared with those of other highly regarded algorithms to ensure a fair and objective performance assessment. The comparative results show that QMOSNS is robust and superior in handling a wide variety of multi-objective problems. This study underscores the efficacy of integrating reinforcement learning with social intelligence for tackling complex multi-objective optimization in engineering and computational domains. | |
| 653 | |a Performance measurement | ||
| 653 | |a Performance assessment | ||
| 653 | |a Social networks | ||
| 653 | |a Exploitation | ||
| 653 | |a Design engineering | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Mutation | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Decision making | ||
| 653 | |a Benchmarks | ||
| 653 | |a Effectiveness | ||
| 653 | |a Pareto optimization | ||
| 653 | |a Search algorithms | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Objectives | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Design optimization | ||
| 653 | |a Pareto optimum | ||
| 653 | |a Optimization algorithms | ||
| 700 | 1 | |a Li Zihan |u School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang 330063, China | |
| 700 | 1 | |a Li, Ji |u School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang 330063, China | |
| 700 | 1 | |a Hu, Guoqing |u School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 22 (2025), p. 3613-3641 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275541980/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275541980/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275541980/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |