{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T21:21:25Z","timestamp":1726435285080},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"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":"In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm\u2019s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.<\/jats:p>","DOI":"10.3390\/s22051999","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T01:40:02Z","timestamp":1646617202000},"page":"1999","source":"Crossref","is-referenced-by-count":1,"title":["A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1528-394X","authenticated-orcid":false,"given":"Yanlei","family":"Yin","sequence":"first","affiliation":[{"name":"Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China"}]},{"given":"Lihua","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5769-1859","authenticated-orcid":false,"given":"Litong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.egyr.2017.10.002","article-title":"Multi-objective energy management in a micro-grid","volume":"4","author":"Aghajani","year":"2018","journal-title":"Energy Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.energy.2017.07.150","article-title":"Electricity load forecasting by an improved forecast engine for building level consumers","volume":"139","author":"Liu","year":"2017","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.engappai.2018.03.022","article-title":"A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm","volume":"72","author":"Hamian","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.aei.2018.02.006","article-title":"A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting","volume":"36","author":"Leng","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1109\/TCYB.2016.2577587","article-title":"A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization","volume":"47","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.asoc.2019.01.043","article-title":"A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems","volume":"77","author":"Truong","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TEVC.2017.2743016","article-title":"A Level-based Learning Swarm Optimizer for Large Scale Optimization","volume":"22","author":"Yang","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_8","first-page":"1439","article-title":"A dynamic co-evolution compact genetic algorithm for E\/T problem","volume":"48","author":"Han","year":"2015","journal-title":"IFAC Pap."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.eswa.2015.12.041","article-title":"Discrete Particle Swarm Optimization Method for the Large-Scale Discrete Time-Cost Trade-Off Problem","volume":"51","author":"Aminbakhsh","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_10","first-page":"2595","article-title":"Dynamic Multi-Swarm Particle Swarm Optimization with Cooperative Coevolution for Large Scale Global Optimization","volume":"29","author":"Liang","year":"2018","journal-title":"Ruan Jian Xue Bao\/J. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"198","DOI":"10.3901\/JME.2015.06.198","article-title":"Hybrid particle interactive particle swarm optimization algorithm","volume":"51","author":"Yao","year":"2015","journal-title":"J. Mech. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.ins.2019.07.016","article-title":"A parameter-free particle swarm optimization algorithm using performance classifiers","volume":"503","author":"Harrison","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/TEVC.2016.2602860","article-title":"Heterogeneous cooperative co-evolution memetic differential evolution algorithm for big data optimization problems","volume":"21","author":"Sabar","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.engappai.2014.02.018","article-title":"A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization","volume":"32","author":"Wang","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.neucom.2018.12.060","article-title":"Escaping the curse of dimensionality in similarity learning: Efficient Frank-Wolfe algorithm and generalization bounds","volume":"333","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_16","unstructured":"Kennedy, J., and Eberhart, R. (December, January 26). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1108\/10662241111104866","article-title":"The perceived benefits of six-degree-separation social networks","volume":"21","author":"Shu","year":"2011","journal-title":"Internet Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.4249\/scholarpedia.1739","article-title":"Small-world network","volume":"7","author":"Porter","year":"2012","journal-title":"Scholarpedia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/4235.771163","article-title":"Evolutionary programming made faster","volume":"3","author":"Yao","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/00207160108805080","article-title":"On benchmarking functions for genetic algorithms","volume":"77","author":"Digalakis","year":"2001","journal-title":"Int. J. Comput. Math."},{"key":"ref_21","unstructured":"Molga, M., and Smutnicki, C. (2021, June 20). Test Functions for Optimization NEEDS. Available online: http:\/\/www.robertmarks.org\/Classes\/ENGR5358\/Papers\/functions.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","article-title":"Firefly algorithm, stochastic test functions and design optimisation","volume":"2","author":"Yang","year":"2010","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"49","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","article-title":"Butterfly optimization algorithm: A novel approach for global optimization","volume":"23","author":"Arora","year":"2019","journal-title":"Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113377","DOI":"10.1016\/j.eswa.2020.113377","article-title":"Marine Predators Algorithm: A Nature-inspired Metaheuristic","volume":"152","author":"Faramarzi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115352","DOI":"10.1016\/j.eswa.2021.115352","article-title":"A new optimization method based on COOT bird natural life model","volume":"183","author":"Naruei","year":"2021","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1999\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T01:32:09Z","timestamp":1722043929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1999"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":26,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051999"],"URL":"https:\/\/doi.org\/10.3390\/s22051999","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}