{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:01:08Z","timestamp":1761897668533,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Swarm Intell"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11721-022-00210-3","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T05:11:09Z","timestamp":1643692269000},"page":"143-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems"],"prefix":"10.1007","volume":"16","author":[{"given":"Pawe\u0142","family":"Jo\u0107ko","sequence":"first","affiliation":[]},{"given":"Beatrice M.","family":"Ombuki-Berman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0242-3539","authenticated-orcid":false,"given":"Andries P.","family":"Engelbrecht","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Branke, J., Salihoglu, E., & Uyar, S. (2005). Towards an analysis of dynamic environments. In Proceedings of the genetic and evolutionary computation conference (pp. 1433\u20131440). ACM. https:\/\/doi.org\/10.1145\/1068009.1068237","key":"210_CR1","DOI":"10.1145\/1068009.1068237"},{"issue":"16\u201318","key":"210_CR2","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1016\/j.neucom.2008.12.041","volume":"72","author":"M C\u00e1mara","year":"2009","unstructured":"C\u00e1mara, M., Lopera, J. O., & de Toro, F. (2009). A single front genetic algorithm for parallel multi-objective optimization in dynamic environments. Neurocomputing, 72(16\u201318), 3570\u20133579. https:\/\/doi.org\/10.1016\/j.neucom.2008.12.041.","journal-title":"Neurocomputing"},{"doi-asserted-by":"publisher","unstructured":"C\u00e1mara, M., Lopera, J. O., & de Toro, F. (2010). Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. In Advances in multi-objective nature inspired computing, studies in computational intelligence (Vol. 272, pp. 63\u201386). Springer. https:\/\/doi.org\/10.1007\/978-3-642-11218-8_4","key":"210_CR3","DOI":"10.1007\/978-3-642-11218-8_4"},{"doi-asserted-by":"publisher","unstructured":"C\u00e1mara, M., Ortega, J., & de Toro, F. (2007). Parallel processing for multi-objective optimization in dynamic environments. In Proceedings of the IEEE international parallel and distributed processing symposium (pp. 1\u20138). https:\/\/doi.org\/10.1109\/IPDPS.2007.370433","key":"210_CR4","DOI":"10.1109\/IPDPS.2007.370433"},{"issue":"2","key":"210_CR5","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"doi-asserted-by":"publisher","unstructured":"Deb, K. Rao N, U. B., & Karthik, S. (2006). Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In 4th international conference on proceedings of the evolutionary multi-criterion optimization, lecture notes in computer science (Vol. 4403, pp. 803\u2013817). Springer. https:\/\/doi.org\/10.1007\/978-3-540-70928-2_60","key":"210_CR6","DOI":"10.1007\/978-3-540-70928-2_60"},{"doi-asserted-by":"publisher","unstructured":"Engelbrecht, A. P. (2013). Particle swarm optimization: Iteration strategies revisited. In Proceedings of the BRICS congress on computational intelligence & 11th Brazilian congress on computational intelligence (pp. 119\u2013123). https:\/\/doi.org\/10.1109\/BRICS-CCI-CBIC.2013.30","key":"210_CR7","DOI":"10.1109\/BRICS-CCI-CBIC.2013.30"},{"doi-asserted-by":"publisher","unstructured":"Erwin, K., & Engelbrecht, A. P. (2019). Control parameter sensitivity analysis of the multi-guide particle swarm optimization algorithm. In Proceedings of the genetic and evolutionary computation conference (pp. 22\u201329). ACM. https:\/\/doi.org\/10.1145\/3321707.3321739","key":"210_CR8","DOI":"10.1145\/3321707.3321739"},{"issue":"5","key":"210_CR9","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TEVC.2004.831456","volume":"8","author":"M Farina","year":"2004","unstructured":"Farina, M., Deb, K., & Amato, P. (2004). Dynamic multiobjective optimization problems: Test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation, 8(5), 425\u2013442. https:\/\/doi.org\/10.1109\/TEVC.2004.831456.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"210_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TEVC.2008.920671","volume":"13","author":"CK Goh","year":"2009","unstructured":"Goh, C. K., & Tan, K. C. (2009). A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 13(1), 103\u2013127. https:\/\/doi.org\/10.1109\/TEVC.2008.920671.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"doi-asserted-by":"publisher","unstructured":"Greeff, M., & Engelbrecht, A. P. (2008). Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation. In Proceedings of the IEEE congress on evolutionary computation (pp. 2917\u20132924). IEEE. https:\/\/doi.org\/10.1109\/CEC.2008.4631190","key":"210_CR11","DOI":"10.1109\/CEC.2008.4631190"},{"doi-asserted-by":"publisher","unstructured":"Harrison, K. R., Ombuki-Berman, B. M., & Engelbrecht, A. P. (2016). A radius-free quantum particle swarm optimization technique for dynamic optimization problems. In Proceedings of the IEEE congress on evolutionary computation (pp. 578\u2013585). IEEE. https:\/\/doi.org\/10.1109\/CEC.2016.7743845","key":"210_CR12","DOI":"10.1109\/CEC.2016.7743845"},{"doi-asserted-by":"publisher","unstructured":"Helbig, M., & Engelbrecht, A. P. (2012). Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems. In Proceedings of the IEEE congress on evolutionary computation (pp. 1\u20138). IEEE. https:\/\/doi.org\/10.1109\/CEC.2012.6252882","key":"210_CR13","DOI":"10.1109\/CEC.2012.6252882"},{"key":"210_CR14","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.swevo.2018.01.006","volume":"41","author":"KR Harrison","year":"2018","unstructured":"Harrison, K. R., Engelbrecht, A. P., & Ombuki-Berman, B. M. (2018). Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm and Evolutionary Computation, 41, 20\u201335. https:\/\/doi.org\/10.1016\/j.swevo.2018.01.006.","journal-title":"Swarm and Evolutionary Computation"},{"doi-asserted-by":"publisher","unstructured":"Helbig, M., & Engelbrecht, A. P. (2013a). Analysing the performance of dynamic multi-objective optimisation algorithms. In Proceedings of the IEEE congress on evolutionary computation (pp. 1531\u20131539). IEEE. https:\/\/doi.org\/10.1109\/CEC.2013.6557744","key":"210_CR15","DOI":"10.1109\/CEC.2013.6557744"},{"doi-asserted-by":"publisher","unstructured":"Helbig, M., & Engelbrecht, A. P. (2013b). Benchmarks for dynamic multi-objective optimisation. In Proceedings of the IEEE symposium on computational intelligence in dynamic and uncertain environments (pp. 84\u201391). IEEE. https:\/\/doi.org\/10.1109\/CIDUE.2013.6595776","key":"210_CR16","DOI":"10.1109\/CIDUE.2013.6595776"},{"key":"210_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.ins.2013.06.051","volume":"250","author":"M Helbig","year":"2013","unstructured":"Helbig, M., & Engelbrecht, A. P. (2013c). Performance measures for dynamic multi-objective optimisation algorithms. Information Sciences, 250, 61\u201381. https:\/\/doi.org\/10.1016\/j.ins.2013.06.051.","journal-title":"Information Sciences"},{"issue":"1","key":"210_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TEVC.2016.2574621","volume":"21","author":"S Jiang","year":"2017","unstructured":"Jiang, S., & Yang, S. (2017). A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 21(1), 65\u201382. https:\/\/doi.org\/10.1109\/TEVC.2016.2574621.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"unstructured":"Jiang, S., Yang, S., Yao, X., Tan, K. C., Kaiser, M., & Krasnogor, N. (2017). Benchmark problems for CEC2018 competition on dynamic multiobjective optimisation. Tech. rep., Newcastle University, School of Computing. http:\/\/www.tech.dmu.ac.uk\/%7Esyang\/TF-ECiDUE\/TR-CEC2018-DMOP-Competition.pdf","key":"210_CR19"},{"doi-asserted-by":"crossref","unstructured":"Jo\u0107ko, P. (2021). Multi-guide particle swarm optimisation for dynamic multi-objective optimisation problems. Master\u2019s thesis, Brock University.","key":"210_CR20","DOI":"10.1007\/s11721-022-00210-3"},{"issue":"2","key":"210_CR21","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s12293-009-0026-7","volume":"2","author":"WT Koo","year":"2010","unstructured":"Koo, W. T., Goh, C. K., & Tan, K. C. (2010). A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment. Memetic Computing, 2(2), 87\u2013110. https:\/\/doi.org\/10.1007\/s12293-009-0026-7.","journal-title":"Memetic Computing"},{"doi-asserted-by":"publisher","unstructured":"Leonard, B. J., & Engelbrecht, A. P. (2013). On the optimality of particle swarm parameters in dynamic environments. In Proceedings of the IEEE congress on evolutionary computation (pp. 1564\u20131569). IEEE. https:\/\/doi.org\/10.1109\/CEC.2013.6557748","key":"210_CR22","DOI":"10.1109\/CEC.2013.6557748"},{"issue":"3\u20134","key":"210_CR23","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s11721-019-00168-9","volume":"13","author":"ET Oldewage","year":"2019","unstructured":"Oldewage, E. T., Engelbrecht, A. P., & Cleghorn, C. W. (2019). Degrees of stochasticity in particle swarm optimization. Swarm Intelligence, 13(3\u20134), 193\u2013215. https:\/\/doi.org\/10.1007\/s11721-019-00168-9.","journal-title":"Swarm Intelligence"},{"doi-asserted-by":"publisher","unstructured":"Pampar\u00e0, G., & Engelbrecht, A. P. (2018). Self-adaptive quantum particle swarm optimization for dynamic environments. In 11th international conference on proceedings of the swarm intelligence, lecture notes in computer science (Vol. 11172, pp. 163\u2013175). Springer. https:\/\/doi.org\/10.1007\/978-3-030-00533-7_13","key":"210_CR24","DOI":"10.1007\/978-3-030-00533-7_13"},{"issue":"3\u20134","key":"210_CR25","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11721-019-00171-0","volume":"13","author":"C Scheepers","year":"2019","unstructured":"Scheepers, C., Engelbrecht, A. P., & Cleghorn, C. W. (2019). Multi-guide particle swarm optimization for multi-objective optimization: Empirical and stability analysis. Swarm Intelligence, 13(3\u20134), 245\u2013276. https:\/\/doi.org\/10.1007\/s11721-019-00171-0.","journal-title":"Swarm Intelligence"},{"unstructured":"Schott, J. (2005). Fault tolerant design using single and multicriteria genetic algorithm optimization. Master\u2019s thesis, Massachusetts Institute of Technology. http:\/\/hdl.handle.net\/1721.1\/11582","key":"210_CR26"},{"doi-asserted-by":"publisher","unstructured":"Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE international conference on evolutionary computation (pp. 69\u201373). https:\/\/doi.org\/10.1109\/ICEC.1998.699146","key":"210_CR27","DOI":"10.1109\/ICEC.1998.699146"},{"doi-asserted-by":"publisher","unstructured":"Weicker, K. (2002). Performance measures for dynamic environments. In Proceedings of the parallel problem solving from nature, lecture notes in computer science (Vol. 2439, pp. 64\u201376). Springer. https:\/\/doi.org\/10.1007\/3-540-45712-7_7","key":"210_CR28","DOI":"10.1007\/3-540-45712-7_7"},{"issue":"5","key":"210_CR29","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1109\/TEVC.2020.2985323","volume":"24","author":"K Zhang","year":"2020","unstructured":"Zhang, K., Shen, C., Liu, X., & Yen, G. G. (2020). Multiobjective evolution strategy for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 24(5), 974\u2013988. https:\/\/doi.org\/10.1109\/TEVC.2020.2985323.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"210_CR30","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/TCYB.2013.2245892","volume":"44","author":"A Zhou","year":"2014","unstructured":"Zhou, A., Jin, Y., & Zhang, Q. (2014). A population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Transactions on Cybernetics, 44(1), 40\u201353. https:\/\/doi.org\/10.1109\/TCYB.2013.2245892.","journal-title":"IEEE Transactions on Cybernetics"},{"doi-asserted-by":"publisher","unstructured":"Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., & Tsang, E. P. K. (2006). Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In 4th international conference on proceedings of the evolutionary multi-criterion optimization, lecture notes in computer science (Vol. 4403, pp. 832\u2013846). Springer. https:\/\/doi.org\/10.1007\/978-3-540-70928-2_62","key":"210_CR31","DOI":"10.1007\/978-3-540-70928-2_62"},{"unstructured":"Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications. PhD thesis, University of Zurich, Z\u00fcrich, Switzerland. http:\/\/d-nb.info\/95814172X","key":"210_CR32"}],"container-title":["Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-022-00210-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11721-022-00210-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-022-00210-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T10:25:10Z","timestamp":1649845510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11721-022-00210-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["210"],"URL":"https:\/\/doi.org\/10.1007\/s11721-022-00210-3","relation":{},"ISSN":["1935-3812","1935-3820"],"issn-type":[{"type":"print","value":"1935-3812"},{"type":"electronic","value":"1935-3820"}],"subject":[],"published":{"date-parts":[[2022,2,1]]},"assertion":[{"value":"3 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}