{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:28:16Z","timestamp":1772645296214,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61806119"],"award-info":[{"award-number":["61806119"]}]},{"name":"National Natural Science Foundation of China","award":["2024JC-YBMS-516"],"award-info":[{"award-number":["2024JC-YBMS-516"]}]},{"name":"National Natural Science Foundation of China","award":["GK202201014"],"award-info":[{"award-number":["GK202201014"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["61806119"],"award-info":[{"award-number":["61806119"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2024JC-YBMS-516"],"award-info":[{"award-number":["2024JC-YBMS-516"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["GK202201014"],"award-info":[{"award-number":["GK202201014"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["61806119"],"award-info":[{"award-number":["61806119"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2024JC-YBMS-516"],"award-info":[{"award-number":["2024JC-YBMS-516"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["GK202201014"],"award-info":[{"award-number":["GK202201014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem\u2019s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks.<\/jats:p>","DOI":"10.3390\/sym17010014","type":"journal-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:19:32Z","timestamp":1735154372000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Solving Multi-Objective Satellite Data Transmission Scheduling Problems via a Minimum Angle Particle Swarm Optimization"],"prefix":"10.3390","volume":"17","author":[{"given":"Zhe","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5129-995X","authenticated-orcid":false,"given":"Shi","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]},{"given":"Yuyuan","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]},{"given":"Zhixin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]},{"given":"Hao","family":"Ran","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6983-4244","authenticated-orcid":false,"given":"Lining","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710126, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100560","DOI":"10.1016\/j.swevo.2019.100560","article-title":"A large-scale multi-objective satellite data transmission scheduling algorithm based on SVM+ NSGA-II","volume":"50","author":"Zhang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s11590-014-0744-8","article-title":"On the tractability of satellite range scheduling","volume":"9","author":"Vazquez","year":"2015","journal-title":"Optim. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0895-7177(96)00093-3","article-title":"Automating Air Force Satellite Control Network (AFSCN) scheduling","volume":"24","author":"Gooley","year":"1996","journal-title":"Math. Comput. Model."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Barbulescu, L., Howe, A.E., Watson, J.P., and Whitley, L.D. (2002, January 7\u201311). Satellite range scheduling: A comparison of genetic, heuristic and local search. Proceedings of the Parallel Problem Solving from Nature\u2014PPSN VII: 7th International Conference, Granada, Spain. Proceedings 7.","DOI":"10.1007\/3-540-45712-7_59"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xia, L., and Yu, N. (2014, January 13\u201316). Redundant and Relay Assistant Scheduling of Small Satellites. Proceedings of the 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, BC, Canada.","DOI":"10.1109\/AINA.2014.124"},{"key":"ref_6","first-page":"279","article-title":"An algorithm based on differential evolution for satellite data transmission scheduling","volume":"18","author":"Liang","year":"2019","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_7","first-page":"2931","article-title":"The inter-satellite data transmission method in satellite networks is based on a hybrid evolutionary algorithm","volume":"45","author":"Yong","year":"2023","journal-title":"Syst. Eng. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.26599\/TST.2023.9010131","article-title":"Data-Driven Collaborative Scheduling Method for Multi-Satellite Data-Transmission","volume":"29","author":"Chen","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105626","DOI":"10.1016\/j.cor.2021.105626","article-title":"An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem","volume":"139","author":"Zhang","year":"2022","journal-title":"Comput. Oper. Res."},{"key":"ref_10","first-page":"3744","article-title":"Dynamic rescheduling method of measurement and control of data transmission resources based on multi-objective optimization","volume":"46","author":"Chen","year":"2024","journal-title":"Syst. Eng. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/PL00013714","article-title":"A model of co-evolutionary design","volume":"16","author":"Maher","year":"2000","journal-title":"Eng. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1007\/s00170-024-14626-0","article-title":"A crayfish optimized wavelet filter and its application to fault diagnosis of machine components","volume":"135","author":"Chauhan","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_13","first-page":"1983","article-title":"Analysing Recent Breakthroughs in Fault Diagnosis through Sensor: A Comprehensive Overview","volume":"141","author":"Chauhan","year":"2024","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101396","DOI":"10.1016\/j.swevo.2023.101396","article-title":"Space division and adaptive selection strategy based differential evolution algorithm for multi-objective satellite range scheduling problem","volume":"83","author":"Wang","year":"2023","journal-title":"Swarm Evol. Comput."},{"key":"ref_15","first-page":"271","article-title":"Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling","volume":"15","author":"Song","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mai, Y., Shi, H., Liao, Q., Sheng, Z., Zhao, S., Ni, Q., and Zhang, W. (2020). Using the decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes and dynamic constraint strategies to retrieve atmospheric ducts. Sensors, 20.","DOI":"10.3390\/s20082230"},{"key":"ref_17","unstructured":"Gong, D.W., Zhang, Y., and Zhang, J.H. (2024, January 5\u20138). Multi-objective particle swarm optimization based on minimal particle angle. Proceedings of the International Conference on Intelligent Computing, Tianjin, China."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.asoc.2021.107299","article-title":"A vector angles-based many-objective particle swarm optimization algorithm using archive","volume":"106","author":"Yang","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108532","DOI":"10.1016\/j.asoc.2022.108532","article-title":"A multi-objective particle swarm optimization algorithm based on a two-archive mechanism","volume":"119","author":"Cui","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1504\/IJGUC.2023.131010","article-title":"Many-objective particle swarm optimization algorithm based on multi-elite opposition mutation mechanism in the Internet of things environment","volume":"14","author":"Kang","year":"2023","journal-title":"Int. J. Grid Util. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1137\/S1052623496307510","article-title":"Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems","volume":"8","author":"Das","year":"1998","journal-title":"SIAM J. Optim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TEVC.2013.2262178","article-title":"Shift-based density estimation for Pareto-based algorithms in many-objective optimization","volume":"18","author":"Li","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1109\/TCYB.2017.2710133","article-title":"An external archive-guided multi-objective particle swarm optimization algorithm","volume":"47","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multi-objective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110162","DOI":"10.1016\/j.asoc.2023.110162","article-title":"A MOEA\/D with global and local cooperative optimization for complicated bi-objective optimization problems","volume":"137","author":"Wang","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s12008-022-00848-7","article-title":"Multi-objective genetic algorithm (MOGA) based optimization of high-pressure coolant assisted hard turning of 42CrMo4 steel","volume":"16","author":"Saha","year":"2022","journal-title":"Int. J. Interact. Des. Manuf. (IJIDeM)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11603","DOI":"10.1007\/s10489-024-05714-5","article-title":"A clustering-based archive handling method and multi-objective optimization of the optimal power flow problem","volume":"54","author":"Akbel","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112155","DOI":"10.1016\/j.asoc.2024.112155","article-title":"Dynamic switched crowding-based multi-objective particle swarm optimization algorithm for solving multi-objective AC-DC optimal power flow problem","volume":"166","author":"Kahraman","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","article-title":"A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems","volume":"22","author":"Yue","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101196","DOI":"10.1016\/j.swevo.2022.101196","article-title":"Unified space approach-based Dynamic Switched Crowding (DSC): A new method for designing Pareto-based multi\/many-objective algorithms","volume":"75","author":"Kahraman","year":"2022","journal-title":"Swarm Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TEVC.2020.2964705","article-title":"A new hypervolume-based evolutionary algorithm for many-objective optimization","volume":"24","author":"Shang","year":"2020","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:00:01Z","timestamp":1760115601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,25]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["sym17010014"],"URL":"https:\/\/doi.org\/10.3390\/sym17010014","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,25]]}}}