{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:06:49Z","timestamp":1780085209603,"version":"3.54.0"},"reference-count":34,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2021YFB2900200"],"award-info":[{"award-number":["2021YFB2900200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel routing planning method based on multi-objective optimization to tackle the routing problem in computing power networks. The proposed method aims to improve the performance and efficiency of routing by considering multiple objectives. In this study, we first model the computing power network and formulate the routing problem as a multi-objective optimization problem. To address this problem, we introduce a non-dominated sorting genetic algorithm incorporating a ratio parameter adjustment strategy based on reinforcement learning. Extensive simulations are conducted to evaluate the performance of the proposed routing algorithm. The results demonstrate significant client latency and cost reductions, highlighting the algorithm\u2019s effectiveness. By providing a comprehensive solution to the routing problem in computing power networks, this work contributes to the field by offering improved performance and efficiency. The proposed method\u2019s ability to optimize multiple objectives sets it apart from existing approaches, making it a valuable contribution to the research community.<\/jats:p>","DOI":"10.3390\/s23156702","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T02:14:48Z","timestamp":1690424088000},"page":"6702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Computing Power Network: Multi-Objective Optimization-Based Routing"],"prefix":"10.3390","volume":"23","author":[{"given":"Yunpeng","family":"Xie","sequence":"first","affiliation":[{"name":"Research Institute China Telecom, Beijing 102209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2571-1979","authenticated-orcid":false,"given":"Xiaoyao","family":"Huang","sequence":"additional","affiliation":[{"name":"Research Institute China Telecom, Beijing 102209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhe","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","article-title":"A survey on deep learning for big data","volume":"42","author":"Zhang","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alsmadi, A.A., Shuhaiber, A., Al-Okaily, M., Al-Gasaymeh, A., and Alrawashdeh, N. (2023). Big data analytics and innovation in e-commerce: Current insights and future directions. J. Financ. Serv. Mark., 1\u201318.","DOI":"10.1057\/s41264-023-00235-7"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15435","DOI":"10.1109\/JIOT.2022.3176400","article-title":"A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges","volume":"9","author":"McEnroe","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10922-021-09618-4","article-title":"A scalable hierarchically distributed architecture for next-generation applications","volume":"30","author":"Ravuri","year":"2022","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_5","first-page":"1","article-title":"Wireless powered mobile edge computing networks: A survey","volume":"55","author":"Wang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ouyang, R., Wang, J., Xu, H., Chen, S., Xiong, X., Tolba, A., and Zhang, X. (2023). A Microservice and Serverless Architecture for Secure IoT System. Sensors, 23.","DOI":"10.3390\/s23104868"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, J., Ouyang, R., Wen, W., Wan, X., Wang, W., Tolba, A., and Zhang, X. (2023). A Post-Evaluation System for Smart Grids Based on Microservice Framework and Big Data Analysis. Electronics, 12.","DOI":"10.3390\/electronics12071647"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, J., Yuan, Q., Chen, S., He, H., Jiang, X., and Tan, X. (2023). Cooperative Task Offloading for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning. IEEE Trans. Netw. Serv. Manag.","DOI":"10.1109\/TNSM.2023.3240415"},{"key":"ref_9","first-page":"1","article-title":"On deep reinforcement learning for static routing and wavelength assignment","volume":"28","author":"Mercan","year":"2022","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104109","DOI":"10.1109\/ACCESS.2020.2995558","article-title":"QROUTE: An efficient quality of service (QoS) routing scheme for software-defined overlay networks","volume":"8","author":"Varyani","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","first-page":"3174716","article-title":"Energy-efficient resource allocation and migration in private cloud data center","volume":"2022","author":"Sharma","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4757","DOI":"10.1109\/TITS.2020.3041746","article-title":"Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular ad hoc networks in intelligent transportation systems","volume":"22","author":"Fatemidokht","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/TSC.2018.2849712","article-title":"Resource aware routing for service function chains in SDN and NFV-enabled network","volume":"14","author":"Pei","year":"2018","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.asoc.2017.07.045","article-title":"Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0","volume":"68","author":"Faheem","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119456","DOI":"10.1016\/j.eswa.2022.119456","article-title":"Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities","volume":"216","author":"Jan","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Varasteh, A., De Andrade, M., Machuca, C.M., Wosinska, L., and Kellerer, W. (2018, January 9\u201313). Power-aware virtual network function placement and routing using an abstraction technique. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647538"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.comcom.2017.04.012","article-title":"Achieving energy efficiency in data centers with a performance-guaranteed power aware routing","volume":"109","author":"Baccour","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1742-6596\/1284\/1\/012053","article-title":"Research on routing algorithm based on reinforcement learning in SDN","volume":"1284","author":"Fang","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119910","DOI":"10.1016\/j.eswa.2023.119910","article-title":"Reinforcement learning-based multi-strategy cuckoo search algorithm for 3D UAV path planning","volume":"223","author":"Yu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1016\/j.ins.2010.05.013","article-title":"Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection","volume":"180","author":"Qu","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.applthermaleng.2018.10.020","article-title":"Multi-objective optimization of the environmental-economic dispatch with reinforcement learning based on non-dominated sorting genetic algorithm","volume":"146","author":"Bora","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_23","unstructured":"Chen, Y., Hu, J.L., Hirasawa, K., and Yu, S. (2007, January 17\u201320). Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning. Proceedings of the SICE Annual Conference 2007, Takamatsu, Japan."},{"key":"ref_24","first-page":"149","article-title":"Multiple policy selection genetic algorithm based on reinforcement learning","volume":"37","author":"Wang","year":"2011","journal-title":"Jisuanji Gongcheng\/Comput. Eng."},{"key":"ref_25","first-page":"246","article-title":"RNSGA-II algorithm supporting reinforcement learning and its application in UAV path planning","volume":"56","author":"Feng","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_26","first-page":"113","article-title":"Reinforcement learning NSGA-II algorithm for multi-objective flexible job shop scheduling","volume":"45","author":"Yin","year":"2022","journal-title":"J. Chongqing Univ."},{"key":"ref_27","first-page":"180","article-title":"A hybrid algorithm based on NSGA-II and differential evolution for multi-objective optimization problems","volume":"56","author":"Li","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_28","first-page":"1","article-title":"A multi-objective evolutionary algorithm based on decomposition and differential evolution for optimization problems with many objectives","volume":"40","author":"Zhang","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2019.01.015","article-title":"A novel multi-objective evolutionary algorithm based on decomposition and reinforcement learning","volume":"174","author":"Li","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","first-page":"1","article-title":"A novel multi-objective evolutionary algorithm based on the decomposition of the objective space and reinforcement learning","volume":"142","author":"Li","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_31","first-page":"1","article-title":"A novel multi-objective evolutionary algorithm based on the decomposition of the objective space and adaptive learning","volume":"166","author":"Liu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1016\/j.ress.2005.11.018","article-title":"Multi-objective optimization using genetic algorithms: A tutorial","volume":"91","author":"Konak","year":"2006","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_33","unstructured":"Lin, L., and Gen, M. (2009). Intelligent and Evolutionary Systems, Springer. Studies in Computational Intelligence."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sonmez, C., Ozgovde, A., and Ersoy, C. (2017, January 21\u201325). Performance evaluation of single-tier and two-tier cloudlet assisted applications. Proceedings of the 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France.","DOI":"10.1109\/ICCW.2017.7962674"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:19:39Z","timestamp":1760127579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":34,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156702"],"URL":"https:\/\/doi.org\/10.3390\/s23156702","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,26]]}}}