{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T20:58:31Z","timestamp":1760821111786,"version":"3.37.3"},"reference-count":53,"publisher":"Informa UK Limited","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572241","61271385"],"award-info":[{"award-number":["61572241","61271385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Connection Science"],"published-print":{"date-parts":[[2020,10,1]]},"DOI":"10.1080\/09540091.2019.1700911","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T12:15:47Z","timestamp":1576152947000},"page":"333-361","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":7,"title":["A novel particle swarm optimisation with mutation breeding"],"prefix":"10.1080","volume":"32","author":[{"given":"Zhe","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, People's Republic of China"},{"name":"Jiangsu Key Laboratory of Security Technology for industrial Cyberspace, Zhenjiang, People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, People's Republic of China"},{"name":"Jiangsu Key Laboratory of Security Technology for industrial Cyberspace, Zhenjiang, People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing-Hua","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, People's Republic of China"},{"name":"Jiangsu Key Laboratory of Security Technology for industrial Cyberspace, Zhenjiang, People's Republic of China"},{"name":"School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"CIT0001","doi-asserted-by":"crossref","unstructured":"Bao, H. & Han, F. (2017). A hybrid multi-swarm PSO algorithm based on shuffled frog leaping algorithm. InLecture Notes in Computer Science(pp. 101\u2013112). Springer International Publishing.","DOI":"10.1007\/978-3-319-67777-4_9"},{"key":"CIT0002","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23285-0_16"},{"key":"CIT0003","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2016.1245729"},{"key":"CIT0004","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2018.2885075"},{"key":"CIT0005","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.07.020"},{"key":"CIT0006","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.08.039"},{"key":"CIT0007","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2102-5"},{"key":"CIT0008","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2560128"},{"key":"CIT0009","doi-asserted-by":"crossref","unstructured":"Eberhart, R. C. & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. InProceedings of Congress Evolutionary Computation. CEC00 (Cat. No.00TH8512)(Vol. 1. pp. 84\u201388).","DOI":"10.1109\/CEC.2000.870279"},{"key":"CIT0010","doi-asserted-by":"crossref","unstructured":"Emara, H. M. (2009). Adaptive clubs-based particle swarm optimization. InProceedings of the American Control Conf(pp. 5628\u20135634).","DOI":"10.1109\/ACC.2009.5160390"},{"key":"CIT0011","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2011.05.046"},{"key":"CIT0012","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-008-9080-4"},{"key":"CIT0013","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2475174"},{"key":"CIT0014","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2018.06.012"},{"key":"CIT0015","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.10.048"},{"key":"CIT0016","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.03.074"},{"key":"CIT0017","doi-asserted-by":"crossref","unstructured":"Han, F. & Liu, Q. (2015). An improved hybrid PSO based on ARPSO and the quasi-newton method. InAdvances in Swarm and Computational Intelligence(pp. 460\u2013467). Springer International Publishing.","DOI":"10.1007\/978-3-319-20466-6_48"},{"key":"CIT0018","doi-asserted-by":"crossref","unstructured":"Han, F. & Zhu, J. (2011). An improved arpso for feedforward neural networks. InProceedings of the Seventh International Conference Natural Computation(Vol. 2, pp. 1146\u20131150).","DOI":"10.1109\/ICNC.2011.6022153"},{"key":"CIT0019","doi-asserted-by":"crossref","unstructured":"Huang, S.Y. & Chen, C.L. (2009). A hybrid particle swarm optimization algorithm with diversity for flow-shop scheduling problem. InProceedings of the Information and Control (ICICIC) 2009 Fourth International Conference Innovative Computing(pp. 864\u2013867).","DOI":"10.1109\/ICICIC.2009.21"},{"key":"CIT0020","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12139"},{"key":"CIT0021","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2008.05.067"},{"key":"CIT0022","doi-asserted-by":"crossref","unstructured":"Kennedy, J. (2003). Bare bones particle swarms. InProc. IEEE Swarm Intelligence Symp. SIS'03 (Cat. No.03EX706)(pp. 80\u201387).","DOI":"10.1109\/SIS.2003.1202251"},{"key":"CIT0023","doi-asserted-by":"crossref","unstructured":"Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. InProc. ICNN'95 - Int. Conf. Neural Networks(Vol. 4. pp. 1942\u20131948.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"CIT0024","doi-asserted-by":"crossref","unstructured":"Kennedy, J. & Mendes, R. (2002). Population structure and particle swarm performance. InProc. Congress Evolutionary Computation. CEC'02 (Cat. No.02TH8600)(Vol. 2. pp. 1671\u20131676).","DOI":"10.1109\/CEC.2002.1004493"},{"key":"CIT0025","doi-asserted-by":"crossref","unstructured":"Krink, T., Vesterstrom, J. S. & Riget, J. (2002). Particle swarm optimisation with spatial particle extension. InProc. Congress Evolutionary Computation. CEC'02 (Cat. No.02TH8600)(Vol. 2. pp. 1474\u20131479).","DOI":"10.1109\/CEC.2002.1004460"},{"key":"CIT0026","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.08.043"},{"key":"CIT0027","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.09.030"},{"key":"CIT0028","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1262-4"},{"key":"CIT0029","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.857610"},{"key":"CIT0030","unstructured":"Liang, J. J., Qu, B. Y., Suganthan, P. N. & Chen, Q. (2014). Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization.CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization."},{"key":"CIT0031","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.03.031"},{"key":"CIT0032","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.09.038"},{"key":"CIT0033","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2014.931355"},{"key":"CIT0034","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911450"},{"key":"CIT0035","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.826074"},{"key":"CIT0036","doi-asserted-by":"crossref","unstructured":"Monson, C. K. & Seppi, K. D. (2006). Adaptive diversity in PSO. In M. Cattolico, (Eds.),Genetic and Evolutionary Computation Conference, (pp. 59\u201366).","DOI":"10.1145\/1143997.1144006"},{"key":"CIT0037","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2018.1525709"},{"key":"CIT0038","doi-asserted-by":"crossref","unstructured":"Nie, R. & Yue, J. (2008). A GA and Particle Swarm Optimization based hybrid algorithm. InProc. IEEE Congress Evolutionary Computation (IEEE World Congress Computational Intelligence)(pp. 1047\u20131050).","DOI":"10.1109\/CEC.2008.4630925"},{"key":"CIT0039","doi-asserted-by":"crossref","unstructured":"Pal, D., Verma, P., Gautam, D. & Indait, P. (2016). Improved optimization technique using hybrid ACO-PSO. InProceedings of the 2nd International Conference on Next Generation Computing Technologies (NGCT)(pp. 277\u2013282).","DOI":"10.1109\/NGCT.2016.7877428"},{"key":"CIT0040","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1109\/JAS.2017.7510697","volume":"6","author":"Pare S.","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"CIT0041","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2014.907555"},{"key":"CIT0042","unstructured":"Riget, J. & Vesterstrom, J. (2002).A diversity-guided particle swarm optimizer - the ARPSO. Technical report, EVALife, Department of Computer Science, University of Aarhus."},{"key":"CIT0043","doi-asserted-by":"crossref","unstructured":"Shi, Y. & Eberhart, R. (1998). A modified particle swarm optimizer. InProceedings of the IEEE World Congress Computational Intelligence (Cat. No.98TH8360), 1998 IEEE International Conference Evolutionary Computation(pp. 69\u201373).","DOI":"10.1109\/ICEC.1998.699146"},{"key":"CIT0044","doi-asserted-by":"publisher","DOI":"10.4018\/IJAMC.2017100107"},{"key":"CIT0045","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.10.004"},{"key":"CIT0046","unstructured":"Tan, L., Chen, Y. & Li, C. (2018). Research on image segmentation optimization algorithm based on chaotic particle swarm optimization and fuzzy clustering. InProceedings of the 7th International Conference on Software and Computer Applications(pp. 178\u2013182)."},{"key":"CIT0047","doi-asserted-by":"crossref","unstructured":"Tan, F. & Xia, B. (2018). Chemical reaction intermediate state kinetic optimization by particle swarm optimization. InAdvances in Swarm Intelligence - 9th International Conference(Vol. 10941. pp. 132\u2013142).","DOI":"10.1007\/978-3-319-93815-8_14"},{"key":"CIT0048","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.09.053"},{"key":"CIT0049","doi-asserted-by":"publisher","DOI":"10.3390\/fi10100099"},{"key":"CIT0050","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2016.1185392"},{"key":"CIT0051","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2015.1005084"},{"key":"CIT0052","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.01.027"},{"key":"CIT0053","doi-asserted-by":"crossref","unstructured":"Yang, X., Niu, J. & Cai, Z. (2018). Chaotic simulated annealing particle swarm optimization algorithm. InProceedings of the 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)(pp. 11\u201319).","DOI":"10.1109\/IMCEC.2018.8469645"}],"container-title":["Connection Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/09540091.2019.1700911","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T14:09:41Z","timestamp":1722175781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/09540091.2019.1700911"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,12]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,10,1]]}},"alternative-id":["10.1080\/09540091.2019.1700911"],"URL":"https:\/\/doi.org\/10.1080\/09540091.2019.1700911","relation":{},"ISSN":["0954-0091","1360-0494"],"issn-type":[{"type":"print","value":"0954-0091"},{"type":"electronic","value":"1360-0494"}],"subject":[],"published":{"date-parts":[[2019,12,12]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ccos20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ccos20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2019-04-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}