{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:59:59Z","timestamp":1760234399288,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.<\/jats:p>","DOI":"10.3390\/s21103356","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:46:14Z","timestamp":1620859574000},"page":"3356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6606-4208","authenticated-orcid":false,"given":"Mustafa Hasan","family":"Albowarab","sequence":"first","affiliation":[{"name":"Fakulti Teknologi Maklumat Dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0059-2942","authenticated-orcid":false,"given":"Nurul Azma","family":"Zakaria","sequence":"additional","affiliation":[{"name":"Fakulti Teknologi Maklumat Dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaheera","family":"Zainal Abidin","sequence":"additional","affiliation":[{"name":"Fakulti Teknologi Maklumat Dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdelaziz, A., Fong, A.T., Gani, A., Garba, U., Khan, S., Akhunzada, A., Talebian, H., and Choo, K.-K.R. (2017). Distributed controller clustering in software defined networks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174715"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e3875","DOI":"10.1002\/dac.3875","article-title":"Nature-inspired meta-heuristic algorithms for solving the load balancing problem in the software-defined network","volume":"32","author":"Hosseinzadeh","year":"2019","journal-title":"Int. J. Commun. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","article-title":"Black hole: A new heuristic optimization approach for data clustering","volume":"222","author":"Hatamlou","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Karagul, K., Sahin, Y., Aydemir, E., and Oral, A. (2019). A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption. Lean and Green Supply Chain Management, Springer.","DOI":"10.1007\/978-3-319-97511-5_6"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ins.2017.09.028","article-title":"Pareto front feature selection based on artificial bee colony optimization","volume":"422","author":"Hancer","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119217","DOI":"10.1016\/j.ijheatmasstransfer.2019.119217","article-title":"Optimization of a double-layered microchannel heat sink with semi-porous-ribs by multi-objective genetic algorithm","volume":"149","author":"Wang","year":"2020","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International Conference on Parallel Problem Solving from Nature, Springer.","DOI":"10.1007\/3-540-45356-3_83"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2729974","article-title":"Many-objective software remodularization using NSGA-III","volume":"24","author":"Mkaouer","year":"2015","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"ref_9","unstructured":"Coello, C.A.C., and Lechuga, M.S. (2002, January 12\u201317). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation. CEC\u201902 (Cat. No.02TH8600), Honolulu, HI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","article-title":"Handling multiple objectives with particle swarm optimization","volume":"8","author":"Coello","year":"2004","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sierra, M.R., and Coello, C.A.C. (2005). Improving PSO-based multi-objective optimization using crowding, mutation and\u2208-dominance. International Conference on Evolutionary Multi-Criterion Optimization, Springer.","DOI":"10.1007\/978-3-540-31880-4_35"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10196","DOI":"10.1109\/ACCESS.2018.2890461","article-title":"Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD","volume":"7","author":"Metiaf","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MNET.2016.1600051NM","article-title":"Software-defined network forensics: Motivation, potential locations, requirements, and challenges","volume":"30","author":"Khan","year":"2016","journal-title":"IEEE Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102856","DOI":"10.1016\/j.jnca.2020.102856","article-title":"A comprehensive survey of load balancing techniques in software-defined network","volume":"174","author":"Hamdan","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chai, R., Xie, D., Luo, L., Chen, Q., and Member, S. (2019). Multi-Objective Optimization-Based Virtual Network Embedding Algorithm for Software-Defined Networking. IEEE Trans. Netw. Serv. Manag.","DOI":"10.1109\/TNSM.2019.2953297"},{"key":"ref_17","first-page":"1001","article-title":"Chaotic salp swarm algorithm for SDN multi-controller networks","volume":"22","author":"Ateya","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bao, N., Zuo, J., Zhu, H., and Bao, X. (2018, January 8\u201311). Multi-objective Optimization for SDN Based Resource Selection. Proceedings of the 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China.","DOI":"10.1109\/ICCT.2018.8600035"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.measurement.2016.10.026","article-title":"A multi-objective software defined network traffic measurement","volume":"95","author":"Tahaei","year":"2017","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"72585","DOI":"10.1109\/ACCESS.2020.2987977","article-title":"Flow-Aware Elephant Flow Detection for Software-Defined Networks","volume":"8","author":"Hamdan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xue, H., Kim, K.T., and Youn, H.Y. (2019). Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization. Sensors, 19.","DOI":"10.3390\/s19020311"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.dcan.2019.10.002","article-title":"On the use of the genetic programming for balanced load distribution in software-defined networks","volume":"5","author":"Jamali","year":"2019","journal-title":"Digit. Commun. Netw."},{"key":"ref_23","first-page":"119","article-title":"Meta-heuristic based framework for workflow load balancing in cloud environment","volume":"11","author":"Kaur","year":"2019","journal-title":"Int. J. Inf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhu, R., Wang, H., Gao, Y., Yi, S., and Zhu, F. (2015). Energy saving and load balancing for SDN based on multi-objective particle swarm optimization. International Conference on Algorithms and Architectures for Parallel Processing, Springer.","DOI":"10.1007\/978-3-319-27137-8_14"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.neucom.2018.11.034","article-title":"A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems","volume":"330","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s10489-019-01496-3","article-title":"Multi-objective particle swarm optimization based on cooperative hybrid strategy","volume":"50","author":"Yu","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.swevo.2017.10.009","article-title":"A multi-objective particle swarm optimization algorithm for community detection in complex networks","volume":"39","author":"Rahimi","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"72","DOI":"10.26555\/ijain.v6i1.366","article-title":"Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)","volume":"6","author":"Rashed","year":"2020","journal-title":"Int. J. Adv. Intell. Informat."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TEVC.2003.810758","article-title":"Performance assessment of multiobjective optimizers: An analysis and review","volume":"7","author":"Zitzler","year":"2003","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14710","DOI":"10.1109\/ACCESS.2018.2812701","article-title":"A hybrid multiobjective particle swarm optimization algorithm based on R2 indicator","volume":"6","author":"Wei","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"168091","DOI":"10.1109\/ACCESS.2019.2954542","article-title":"A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.ins.2019.11.047","article-title":"A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization","volume":"514","author":"Luo","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TEVC.2016.2592479","article-title":"A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization","volume":"21","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87916","DOI":"10.1109\/ACCESS.2019.2925540","article-title":"Multiobjective Particle Swarm Optimization Algorithm Based on Adaptive Angle Division","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5013","DOI":"10.1016\/j.ijleo.2016.02.045","article-title":"Multi-objective particle optimization algorithm based on sharing--learning and dynamic crowding distance","volume":"127","author":"Peng","year":"2016","journal-title":"Optik"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.bdr.2018.03.001","article-title":"A direction aware particle swarm optimization with sensitive swarm leader","volume":"14","author":"Mishra","year":"2018","journal-title":"Big Data Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00158-017-1764-7","article-title":"A new PSO-based algorithm for multi-objective optimization with continuous and discrete design variables","volume":"57","author":"Mokarram","year":"2018","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_38","first-page":"16","article-title":"An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line","volume":"2017","author":"Fan","year":"2017","journal-title":"Shock Vib."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100569","DOI":"10.1016\/j.swevo.2019.100569","article-title":"A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization","volume":"50","author":"Zhang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1016\/j.aej.2017.06.017","article-title":"Multi-swarm multi-objective optimization based on a hybrid strategy","volume":"57","author":"Sedarous","year":"2018","journal-title":"Alexandria Eng. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ins.2018.01.038","article-title":"A diversity enhanced multiobjective particle swarm optimization","volume":"436","author":"Pan","year":"2018","journal-title":"Inf. Sci. (Ny)"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5475","DOI":"10.3390\/e15125475","article-title":"Entropy diversity in multi-objective particle swarm optimization","volume":"15","author":"Pires","year":"2013","journal-title":"Entropy"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, Y., Gao, Y., and Shi, X. (2019). Chaotic multi-objective particle swarm optimization algorithm incorporating clone immunity. Mathematics, 7.","DOI":"10.3390\/math7020146"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s11063-016-9496-z","article-title":"A kind of parameters self-adjusting extreme learning machine","volume":"44","author":"Niu","year":"2016","journal-title":"Neural Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.orhc.2018.11.002","article-title":"Optimizing the master surgery schedule in a private hospital","volume":"20","author":"Marques","year":"2019","journal-title":"Oper. Res. Heal. Care"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s10845-020-01547-4","article-title":"A research survey: Heuristic approaches for solving multi objective flexible job shop problems","volume":"31","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_48","unstructured":"Liang, J.J., and Suganthan, P.N. (2006, January 16\u201321). Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3356\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:37Z","timestamp":1760162377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,12]]},"references-count":48,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103356"],"URL":"https:\/\/doi.org\/10.3390\/s21103356","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,5,12]]}}}