{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:28:07Z","timestamp":1775068087501,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Security Management Technology Group (SMT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper proposes a novel hybrid metaheuristic, called JADEGMO, that combines the adaptive parameter control of adaptive differential evolution with optional external archive (JADE) with the search strategies of geometric mean optimizer (GMO). The goal is to enhance both exploration and exploitation stratifies for solving complex optimization tasks. JADEGMO inherits JADE\u2019s adaptive mutation and crossover strategies while leveraging GMO\u2019s swarm-inspired velocity updates guided by elite solutions. The experimental evaluations on IEEE CEC2022 benchmark suites demonstrate that JADEGMO not only achieves superior average performance compared to multiple state-of-the-art methods but also exhibits low variance across repeated runs. Convergence curves, box plots, and rank analyses confirm that JADEGMO consistently finds high-quality solutions while maintaining diversity and avoiding premature convergence. To highlight its applicability, we employ JADEGMO in a real-world multi-cloud security configuration scenario. This problem models the trade-offs among baseline risk, encryption overhead, open ports, privilege levels, and subscription-based security features across three cloud platforms. JADEGMO outperforms other common metaheuristics in locating cost-efficient configurations that minimize risk while balancing overhead and subscription expenses.<\/jats:p>","DOI":"10.3390\/sym17040503","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:06:35Z","timestamp":1743141995000},"page":"503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Cloud Security Optimization Using Novel Hybrid JADE-Geometric Mean Optimizer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4930-0074","authenticated-orcid":false,"given":"Ahmad K.","family":"Al Hwaitat","sequence":"first","affiliation":[{"name":"King Abdullah the II IT School, Department of Computer Science, The University of Jordan, Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-3291","authenticated-orcid":false,"given":"Hussam N.","family":"Fakhouri","sequence":"additional","affiliation":[{"name":"Data Science and Artificial Intelligence Department, Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1007\/s00607-024-01287-w","article-title":"Four vector intelligent metaheuristic for data optimization","volume":"106","author":"Fakhouri","year":"2024","journal-title":"Computing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1145\/937503.937505","article-title":"Metaheuristics in combinatorial optimization: Overview and conceptual comparison","volume":"35","author":"Blum","year":"2003","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/0952813X.2013.782347","article-title":"Metaheuristics: Review and application","volume":"25","author":"Gogna","year":"2013","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1007\/s11227-014-1376-6","article-title":"Efficient task scheduling algorithms for heterogeneous multi-cloud environment","volume":"71","author":"Panda","year":"2015","journal-title":"J. Supercomput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hwaitat, A.K.A., and Fakhouri, H.N. (2024). The OX optimizer: A novel optimization algorithm and its application in enhancing support vector machine performance for attack detection. Symmetry, 16.","DOI":"10.3390\/sym16080966"},{"key":"ref_6","first-page":"289","article-title":"Multi-Cloud Management Strategies\u2014A Comprehensive Review","volume":"9","author":"Dubey","year":"2019","journal-title":"Res. Rev. J. Embed. Syst. Appl."},{"key":"ref_7","first-page":"600","article-title":"Multi-Cloud Strategies for Distributed AI Workflows and Application","volume":"10","author":"Sekar","year":"2023","journal-title":"J. Emerg. Technol. Innov. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S1361-3723(20)30052-X","article-title":"A multi-cloud world requires a multi-cloud security approach","volume":"2020","author":"Duncan","year":"2020","journal-title":"Comput. Fraud. Secur."},{"key":"ref_9","unstructured":"Salhi, S. (1998). Heuristic Search Methods, Lawrence Erlbaum Associates."},{"key":"ref_10","unstructured":"Shanmugapriya, M., and Manivannan, K.K. (2024). Compare the Performance of Meta-Heuristics Algorithm: A Review. Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, IGI Global."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1007\/s10586-023-04161-0","article-title":"Novel hybrid success history intelligent optimizer with Gaussian transformation: Application in CNN hyperparameter tuning","volume":"27","author":"Fakhouri","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fakhouri, H.N., Alawadi, S., Awaysheh, F.M., Hani, I.B., Alkhalaileh, M., and Hamad, F. (2023). A comprehensive study on the role of machine learning in 5G security: Challenges, technologies, and solutions. Electronics, 12.","DOI":"10.3390\/electronics12224604"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shirvani, M.H. (2018, January 3\u20135). Web Service Composition in multi-cloud environment: A bi-objective genetic optimization algorithm. Proceedings of the 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, Greece.","DOI":"10.1109\/INISTA.2018.8466267"},{"key":"ref_14","first-page":"5562","article-title":"QOS Aware Web Services Composition Problem in Multi-Cloud Environment Using Hybrid Optimization Algorithm","volume":"100","author":"Amirthayogam","year":"2022","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/978-3-319-94376-3_9","article-title":"A multi-stage dynamic game-theoretic approach for multi-workflow scheduling on heterogeneous virtual machines from multiple infrastructure-as-a-service clouds","volume":"10969","author":"Wang","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Toinard, C., Ravier, T., C\u00e9rin, C., and Ngoko, Y. (2015, January 25\u201329). The PROMETHEE Method for Cloud Brokering with Trust and Assurance Criteria. Proceedings of the 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Hyderabad, India.","DOI":"10.1109\/IPDPSW.2015.63"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.future.2017.02.004","article-title":"Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing","volume":"71","author":"Entrialgo","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Peng, G., and Qiu, M. (2020, January 1\u20133). Cost Minimization for Music Uploading to a Cloudlet. Proceedings of the 2020 7th IEEE International Conference on Cyber Security and Cloud Computing and 2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud-EdgeCom), New York, NY, USA.","DOI":"10.1109\/CSCloud-EdgeCom49738.2020.00036"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kaur, R., Anand, D., Kaur, U., and Verma, S. (2023, January 7\u20138). Analysis and Evaluation of Bio-Inspired Algorithmic Framework, Potential Application in Cloud\/Multi-Cloud Environment. Proceedings of the 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany.","DOI":"10.1109\/ICCCMLA58983.2023.10346828"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cui, J., Chen, P., and Yu, G. (2020, January 2\u20134). A learning-based dynamic load balancing approach for microservice systems in multi-cloud environment. Proceedings of the International Conference on Parallel and Distributed Systems (ICPADS), Hong Kong.","DOI":"10.1109\/ICPADS51040.2020.00052"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.ins.2018.04.081","article-title":"Security-by-design in multi-cloud applications: An optimization approach","volume":"454\u2013455","author":"Casola","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"John, J.C., Sural, S., and Gupta, A. (2017, January 25\u201330). Optimal Rule Mining for Dynamic Authorization Management in Collaborating Clouds Using Attribute-Based Access Control. Proceedings of the 2017 IEEE International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA.","DOI":"10.1109\/CLOUD.2017.104"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105649","DOI":"10.1016\/j.asoc.2019.105649","article-title":"Secure and economical multi-cloud storage policy with NSGA-II-C","volume":"83","author":"Yang","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ramamurthy, A., Saurabh, S., Gharote, M., and Lodha, S. (2020, January 18\u201324). Selection of cloud service providers for hosting web applications in a multi-cloud environment. Proceedings of the 2020 IEEE 13th International Conference on Services Computing (SCC), Beijing, China.","DOI":"10.1109\/SCC49832.2020.00034"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1109\/TNSM.2022.3227767","article-title":"Knowledge-Engineered Multi-Cloud Resource Brokering for Application Workflow Optimization","volume":"20","author":"Pandey","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Addya, S.K., Satpathy, A., Chakraborty, S., and Ghosh, S.K. (2019, January 7\u20131). Optimal VM Coalition for Multi-Tier Applications over Multi-Cloud Broker Environments. Proceedings of the 2019 11th International Conference on Communication Systems & Networks (COMSNETS), Bangalore, India.","DOI":"10.1109\/COMSNETS.2019.8711038"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"51330","DOI":"10.1109\/ACCESS.2024.3386169","article-title":"Optimized Encryption-Integrated Strategy for Containers Scheduling and Secure Migration in Multi-Cloud Data Centers","volume":"12","author":"Altahat","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.21105\/joss.03262","article-title":"DataLad: Distributed system for joint management of code, data, and their relationship","volume":"6","author":"Halchenko","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1504\/IJWMC.2024.139671","article-title":"Task scheduling in multi-cloud environment via improved optimisation theory","volume":"27","author":"Jawade","year":"2024","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1007\/s11042-023-15687-1","article-title":"DAGWO based secure task scheduling in Multi-Cloud environment with risk probability","volume":"83","author":"Jawade","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/978-3-319-15618-7_8","article-title":"Idea: Optimising multi-cloud deployments with security controls as constraints","volume":"8978","author":"Massonet","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_32","first-page":"55","article-title":"Enabling public verifiability and availability for secure data storage in cloud computing","volume":"6","author":"Jogdand","year":"2015","journal-title":"Evol. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ijinfomgt.2017.07.007","article-title":"Cloud computing research: A review of research themes, frameworks, methods, and future research directions","volume":"38","author":"Senyo","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1007\/s12652-021-03561-7","article-title":"A metaheuristic-based computation offloading in edge-cloud environment","volume":"13","author":"Shahidinejad","year":"2022","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.swevo.2018.03.008","article-title":"Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy","volume":"43","author":"Neri","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10841","DOI":"10.1007\/s00521-021-06216-y","article-title":"An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems","volume":"33","author":"Rauf","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TETCI.2019.2939373","article-title":"Differential evolution with a variable population size for deployment optimization in a UAV-assisted IoT data collection system","volume":"4","author":"Huang","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_38","first-page":"411","article-title":"Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work","volume":"12","author":"Fotovatikhah","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_39","unstructured":"Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., and Clune, J. (2017). Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e4899","DOI":"10.1002\/dac.4899","article-title":"Energy aware cloud-edge service placement approaches in the Internet of Things communications","volume":"35","author":"Heng","year":"2022","journal-title":"Int. J. Commun. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/TETCI.2020.3007905","article-title":"A review on computational intelligence techniques in cloud and edge computing","volume":"4","author":"Asim","year":"2020","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhu, J., and Nie, F. (2024). A novel hybrid adaptive differential evolution for global optimization. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-70731-w"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:02:23Z","timestamp":1760029343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,26]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040503"],"URL":"https:\/\/doi.org\/10.3390\/sym17040503","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,26]]}}}