{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:05:24Z","timestamp":1763535924507,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100014219","name":"national science fund for distinguished young scholars","doi-asserted-by":"crossref","award":["61725306"],"award-info":[{"award-number":["61725306"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["62003370"],"award-info":[{"award-number":["62003370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Changsha Municipal Natural Science Foundation","award":["kq2014137"],"award-info":[{"award-number":["kq2014137"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s10489-021-03005-x","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:03:06Z","timestamp":1641859386000},"page":"10161-10180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions"],"prefix":"10.1007","volume":"52","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yongfang","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Shiwen","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xiaofang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"3005_CR1","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ins.2012.02.016","volume":"197","author":"H Wang","year":"2012","unstructured":"Wang H, Moon I, Yang S et al (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Inform Sci 197:38\u201352","journal-title":"Inform Sci"},{"key":"3005_CR2","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1109\/TMAG.2006.871568","volume":"42","author":"J Seo","year":"2006","unstructured":"Seo J, Im C, Heo C et al (2006) Multimodal function optimization based on particle swarm optimization. IEEE Trans Magn 42:1095\u20131098","journal-title":"IEEE Trans Magn"},{"key":"3005_CR3","first-page":"2449","volume":"6","author":"Y Liu","year":"2011","unstructured":"Liu Y, Ling X, Shi Z et al (2011) A survey on particle swarm optimization algorithms for multimodal function optimization. J Softw 6:2449\u20132455","journal-title":"J Softw"},{"issue":"1","key":"3005_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/TSM.2017.2758380","volume":"31","author":"T Jamrus","year":"2017","unstructured":"Jamrus T, Chien CF, Gen M et al (2017) Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing. IEEE Trans Semicond Manuf 31(1):32\u201341","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"11","key":"3005_CR5","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.1080\/00207160.2017.1387252","volume":"95","author":"S Khan","year":"2018","unstructured":"Khan S, Kamran M, Rehman OU et al (2018) A modified PSO algorithm with dynamic parameters for solving complex engineering design problem. Int J Comput Math 95(11):2308\u20132329","journal-title":"Int J Comput Math"},{"issue":"4","key":"3005_CR6","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1109\/TEVC.2018.2878536","volume":"23","author":"W Liu","year":"2018","unstructured":"Liu W, Wang Z, Liu X et al (2018) A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans Evol Comput 23(4):632\u2013644","journal-title":"IEEE Trans Evol Comput"},{"key":"3005_CR7","doi-asserted-by":"crossref","unstructured":"Xie Y, Xie S, Chen XX et al, Caccetta L (2015) An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy. Hydrometallurgy 151(1):62\u201372","DOI":"10.1016\/j.hydromet.2014.11.004"},{"key":"3005_CR8","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TEVC.2018.2885075","volume":"23","author":"Y Cao","year":"2018","unstructured":"Cao Y, Zhang H, Li W, Chaovalitwongse WA et al (2018) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evol Comput 23:718\u2013731","journal-title":"IEEE Trans Evol Comput"},{"key":"3005_CR9","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: International conference on neural networks, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"5","key":"3005_CR10","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/TEVC.2017.2753538","volume":"22","author":"D Corus","year":"2017","unstructured":"Corus D, Dang D, Eremeev A et al (2017) Level-based analysis of genetic algorithms and other search processes. IEEE Trans Evol Comput 22(5):707\u2013719","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"3005_CR11","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1109\/TKDE.2019.2899096","volume":"32","author":"S Lee","year":"2019","unstructured":"Lee S, Kim SB (2019) Parallel simulated annealing with a greedy algorithm for bayesian network structure learning. IEEE Trans Knowl Data Eng 32(6):1157\u20131166","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"3005_CR12","first-page":"718","volume":"23","author":"J Liang","year":"2018","unstructured":"Liang J, Qin AK, Suganthan PN et al (2018) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evol Comput 23(4):718\u2013731","journal-title":"IEEE Trans Evol Comput"},{"key":"3005_CR13","doi-asserted-by":"crossref","unstructured":"Zhang J, Huang D, Liu K (2007) Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization. In: 2007 IEEE congress on evolutionary computation, pp 3215\u20133220","DOI":"10.1109\/CEC.2007.4424883"},{"key":"3005_CR14","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Congress on evolutionary computation, pp 1945\u20131950","DOI":"10.1109\/CEC.1999.785511"},{"issue":"3","key":"3005_CR15","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s12293-013-0111-9","volume":"5","author":"P Chauhan","year":"2013","unstructured":"Chauhan P, Deep K, Pant M (2013) Novel inertia weight strategies for particle swarm optimization. Memet Comput 5(3):229\u2013251","journal-title":"Memet Comput"},{"key":"3005_CR16","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/TGCN.2021.3067374","volume":"5","author":"Y Huang","year":"2021","unstructured":"Huang Y, Xu H, Gao H, Ma X, Hussain W (2021) SSUR: an approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center. IEEE Trans Green Commun Netw 5:670\u2013681","journal-title":"IEEE Trans Green Commun Netw"},{"key":"3005_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-018-1318-8","volume":"2019","author":"X Ma","year":"2019","unstructured":"Ma X, Gao H, Xu H, Bian M (2019) An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing. EURASIP J Wirel Commun Netw 2019:1\u201319","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"3005_CR18","doi-asserted-by":"crossref","first-page":"155014771876158","DOI":"10.1177\/1550147718761583","volume":"14","author":"H Gao","year":"2018","unstructured":"Gao H, Zhang K, Yang J, Wu F, Liu H (2018) Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks. Int J Distrib Sensor Netw 14:1550147718761583","journal-title":"Int J Distrib Sensor Netw"},{"issue":"11","key":"3005_CR19","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.asoc.2017.06.039","volume":"60","author":"W Chang","year":"2017","unstructured":"Chang W (2017) Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm. Appl Soft Comput 60(11):60\u201372","journal-title":"Appl Soft Comput"},{"issue":"10","key":"3005_CR20","doi-asserted-by":"publisher","first-page":"103905","DOI":"10.1016\/j.engappai.2020.103905","volume":"95","author":"X Zhang","year":"2020","unstructured":"Zhang X, Liu H, Tu L (2020) A modified particle swarm optimization for multimodal multi-objective optimization. Eng Appl Artif Intell 95(10):103905","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"3005_CR21","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ins.2018.01.027","volume":"436","author":"F Wang","year":"2018","unstructured":"Wang F, Zhang H, Li K et al (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inform Sci 436(4):162\u2013177","journal-title":"Inform Sci"},{"key":"3005_CR22","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JAS.2019.1911348","volume":"6","author":"J Wang","year":"2019","unstructured":"Wang J, Kumbasar T (2019) Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods. IEEE\/CAA J Automat Sin 6:247\u2013257","journal-title":"IEEE\/CAA J Automat Sin"},{"issue":"3","key":"3005_CR23","first-page":"627","volume":"42","author":"C Li","year":"2011","unstructured":"Li C, Yang S, Nguyen TT (2011) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(3):627\u2013646","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybernetics)"},{"key":"3005_CR24","unstructured":"Wang X-H, Li J-J (2004) Hybrid particle swarm optimization with simulated annealing. In: International conference on machine learning and cybernetics, pp 2402\u20132405"},{"issue":"6","key":"3005_CR25","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"D Simon","year":"2008","unstructured":"Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702\u2013713","journal-title":"IEEE Trans Evol Comput"},{"key":"3005_CR26","doi-asserted-by":"crossref","unstructured":"Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: International conference on parallel problem solving from nature, pp 249\u2013257","DOI":"10.1007\/3-540-58484-6_269"},{"issue":"3","key":"3005_CR27","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TEVC.2004.826069","volume":"8","author":"F Van den Bergh","year":"2004","unstructured":"Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225\u2013239","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"3005_CR28","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JAS.2019.1911348","volume":"6","author":"J Wang","year":"2019","unstructured":"Wang J, Kumbasar T (2019) Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods. IEEE\/CAA J Automat Sin 6(1):247\u2013257","journal-title":"IEEE\/CAA J Automat Sin"},{"issue":"2","key":"3005_CR29","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","volume":"30","author":"S Gao","year":"2018","unstructured":"Gao S, Zhou M, Wang Y, Cheng J, Yachi H, Wang J (2018) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans Neural Netw Learn Syst 30(2):601\u2013614","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3005_CR30","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Congress on evolutionary computation, pp 1945\u20131950","DOI":"10.1109\/CEC.1999.785511"},{"issue":"4","key":"3005_CR31","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1016\/j.asoc.2011.01.037","volume":"11","author":"A Nickabadi","year":"2011","unstructured":"Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658\u20133670","journal-title":"Appl Soft Comput"},{"key":"3005_CR32","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: International symposium on micro machine and human science, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"3005_CR33","unstructured":"Liang J, Suganthan PN Dynamic multi-swarm particle swarm optimizer, pp 124\u2013129"},{"key":"3005_CR34","doi-asserted-by":"crossref","unstructured":"Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Swarm intelligence symposium, pp 174\u2013181","DOI":"10.1109\/SIS.2003.1202264"},{"key":"3005_CR35","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.ins.2017.01.038","volume":"394","author":"Q Zhang","year":"2017","unstructured":"Zhang Q et al (2017) Vector coevolving particle swarm optimization algorithm. Inf Sci 394:273\u2013298","journal-title":"Inf Sci"},{"key":"3005_CR36","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.asoc.2014.12.026","volume":"29","author":"X Xu","year":"2015","unstructured":"Xu X et al (2015) Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. Appl Soft Comput 29:169\u2013183","journal-title":"Appl Soft Comput"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03005-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03005-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03005-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T07:26:00Z","timestamp":1655709960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03005-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":36,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["3005"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03005-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,1,11]]},"assertion":[{"value":"12 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}