{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:34:58Z","timestamp":1743118498856,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031201752"},{"type":"electronic","value":"9783031201769"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20176-9_33","type":"book-chapter","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T20:03:45Z","timestamp":1666987425000},"page":"360-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Stability-Guided Particle Swarm Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0242-3539","authenticated-orcid":false,"given":"Andries","family":"Engelbrecht","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"33_CR1","unstructured":"Adorio, E.: MVF \u2013 Multivariate Test Functions Library in C for Unconstrained Global Optimization. Technical report. University of the Philippines Diliman (2005)"},{"key":"33_CR2","unstructured":"Al-Roomi, A.: Unconstrained Single-Objective Benchmark Functions Repository (2015). https:\/\/www.al-roomi.org\/benchmarks\/unconstrained"},{"key":"33_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-540-75514-2_9","volume-title":"Hybrid Metaheuristics","author":"P Balaprakash","year":"2007","unstructured":"Balaprakash, P., Birattari, M., St\u00fctzle, T.: Improvement strategies for the F-Race algorithm: sampling design and iterative refinement. In: Bartz-Beielstein, T., et al. (eds.) HM 2007. LNCS, vol. 4771, pp. 108\u2013122. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-75514-2_9"},{"key":"33_CR4","unstructured":"Beielstein, T., Parsopoulos, K.E., Vrahatis, M.N.: Tuning PSO parameters through sensitivity analysis. Universit\u00e4tsbibliothek Dortmund (2002)"},{"key":"33_CR5","unstructured":"Birattari, M., St\u00ebtzle, T., Paquete, L., Varrentrapp, K.: Racing algorithm for configuring metaheuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 11\u201318 (2002)"},{"issue":"3","key":"33_CR6","first-page":"378","volume":"21","author":"MR Bonyadi","year":"2016","unstructured":"Bonyadi, M.R., Michalewicz, Z.: Impacts of coefficients on movement patterns in the particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 21(3), 378\u2013390 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: 2007 IEEE Swarm Intelligence Symposium, pp. 120\u2013127. IEEE (2007)","DOI":"10.1109\/SIS.2007.368035"},{"issue":"1","key":"33_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11721-013-0090-y","volume":"8","author":"C Cleghorn","year":"2014","unstructured":"Cleghorn, C., Engelbrecht, A.: A generalized theoretical deterministic particle swarm model. Swarm Intell. 8(1), 35\u201359 (2014)","journal-title":"Swarm Intell."},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Cleghorn, C., Engelbrecht, A.: Particle swarm convergence: an empirical investigation. In: Proceedings of the IEEE Congress on Evolutionary Computation (2014)","DOI":"10.1109\/CEC.2014.6900439"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Cleghorn, C., Engelbrecht, A.: Particle swarm optimizer: the impact of unstable particles on performance. In: Proceedings of the IEEE Swarm Intelligence Symposium (2016)","DOI":"10.1109\/SSCI.2016.7850265"},{"issue":"1","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11721-017-0141-x","volume":"12","author":"C Cleghorn","year":"2018","unstructured":"Cleghorn, C., Engelbrecht, A.: Particle swarm stability a theoretical extension using the non-stagnate distribution assumption. Swarm Intell. 12(1), 1\u201322 (2018)","journal-title":"Swarm Intell."},{"issue":"1","key":"33_CR12","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/4235.985692","volume":"6","author":"M Clerc","year":"2002","unstructured":"Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58\u201373 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"33_CR13","first-page":"213","volume":"64","author":"F Dobslaw","year":"2010","unstructured":"Dobslaw, F.: A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. Int. J. Aerosp. Mech. Eng. 64, 213\u2013216 (2010)","journal-title":"Int. J. Aerosp. Mech. Eng."},{"key":"33_CR14","unstructured":"Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (2000)"},{"issue":"2","key":"33_CR15","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10462-015-9445-7","volume":"45","author":"A Engelbrecht","year":"2016","unstructured":"Engelbrecht, A.: Particle swarm optimization with crossover: a review and empirical analysis. Artif. Intell. Rev. 45(2), 131\u2013165 (2016)","journal-title":"Artif. Intell. Rev."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Engelbrecht, A.: Inertia weight control strategies: particle roaming behavior. In: International Conference on Soft Computing and Machine Intelligence (2017)","DOI":"10.1109\/ISCMI.2017.8279625"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Erwin, K., Engelbrecht, A.: A tuning free approach to multi-guide particle swarm optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium (2021)","DOI":"10.1109\/SSCI50451.2021.9660050"},{"key":"33_CR18","unstructured":"Gavana, A.: Global Optimisation Benchmarks. http:\/\/infinity77.net\/global_optimization\/index.html. Accessed 31 Mar 2022"},{"issue":"4","key":"33_CR19","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s11721-016-0128-z","volume":"10","author":"K Harrison","year":"2016","unstructured":"Harrison, K., Engelbrecht, A., Ombuki-Berman, B.: Inertia control strategies for particle swarm optimization: too much momentum, not enough analysis. Swarm Intell. 10(4), 267\u2013305 (2016)","journal-title":"Swarm Intell."},{"key":"33_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.swevo.2018.01.006","volume":"41","author":"K Harrison","year":"2018","unstructured":"Harrison, K., Engelbrecht, A., Ombuki-Berman, B.: Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm Evol. Comput. 41, 20\u201335 (2018)","journal-title":"Swarm Evol. Comput."},{"key":"33_CR21","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s11721-017-0150-9","volume":"12","author":"K Harrison","year":"2018","unstructured":"Harrison, K., Engelbrecht, A., Ombuki-Berman, B.: Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intell. 12, 187\u2013226 (2018)","journal-title":"Swarm Intell."},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Harrison, K., Ombuki-Berman, B., Engelbrecht, A.: Optimal parameter regions for particle swarm optimization algorithms. In: Proceedings of the IEEE Congress on Evolutionary Computation (2017)","DOI":"10.1109\/CEC.2017.7969333"},{"key":"33_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-030-26369-0_9","volume-title":"Advances in Swarm Intelligence","author":"KR Harrison","year":"2019","unstructured":"Harrison, K.R., Ombuki-Berman, B.M., Engelbrecht, A.P.: An analysis of control parameter importance in the particle swarm optimization algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11655, pp. 93\u2013105. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26369-0_9"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Harrison, K., Ombuki-Berman, B., Engelbrecht, A.: The parameter configuration landscape: a case study on particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (2019)","DOI":"10.1109\/CEC.2019.8790242"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Jain, N., Nangia, U., Jain, J.: Impact of particle swarm optimization parameters on its convergence. In: Proceedings of the 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, pp. 921\u2013926 (2018)","DOI":"10.1109\/ICPEICES.2018.8897286"},{"issue":"2","key":"33_CR26","first-page":"150","volume":"4","author":"M Jamil","year":"2013","unstructured":"Jamil, M., Yang, X.S.: A literature survey of benchmark functions for global optimization problems. Int. J. Math. Model. Numer. Optim. 4(2), 150\u2013194 (2013)","journal-title":"Int. J. Math. Model. Numer. Optim."},{"issue":"1","key":"33_CR27","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.ipl.2006.10.005","volume":"102","author":"M Jiang","year":"2007","unstructured":"Jiang, M., Luo, Y., Yang, S.: Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf. Process. Lett. 102(1), 8\u201316 (2007)","journal-title":"Inf. Process. Lett."},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Klazar, R., Engelbrecht, A.: Parameter optimization by means of statistical quality guides in F-Race. In: Proceedings of the IEEE Congress on Evolutionary Computation (2014)","DOI":"10.1109\/CEC.2014.6900446"},{"key":"33_CR30","unstructured":"Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report. Tech. Rep. 201311. Zhengzhou University and Nanyang Technological University (2013)"},{"key":"33_CR31","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1016\/j.ins.2019.09.057","volume":"512","author":"E Oldewage","year":"2020","unstructured":"Oldewage, E., Engelbrecht, A., Cleghorn, C.: Movement patterns of a particle swarm in high dimensions. Inf. Sci. 512, 1043\u20131062 (2020)","journal-title":"Inf. Sci."},{"key":"33_CR32","unstructured":"Pedersen, M.: Good parameters for particle swarm optimization. Technical report. HL1001. Hvass Laboratories (2010)"},{"issue":"4","key":"33_CR33","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2008.2011744","volume":"14","author":"R Poli","year":"2009","unstructured":"Poli, R.: Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans. Evol. Comput. 14(4), 712\u2013721 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Poli, R., Broomhead, D.: Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 134\u2013141 (2007)","DOI":"10.1145\/1276958.1276977"},{"key":"33_CR35","doi-asserted-by":"crossref","unstructured":"Scheepers, C., Engelbrecht, A.P., Cleghorn, C.W.: Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis. Swarm Intell. 13(3\u20134), 245\u2013276 (2019)","DOI":"10.1007\/s11721-019-00171-0"},{"key":"33_CR36","unstructured":"Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation Proceedings, pp. 69\u201373 (1998)"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Smith, S., Eiben, A.: Comparing parameter tuning methods for evolutionary algorithms. In: Proceedings of the IEEE Congress on Evolutionary Computation (2009)","DOI":"10.1109\/CEC.2009.4982974"},{"issue":"8","key":"33_CR38","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1016\/j.ins.2005.02.003","volume":"176","author":"F Van den Bergh","year":"2006","unstructured":"Van den Bergh, F., Engelbrecht, A.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937\u2013971 (2006)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20176-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T20:14:13Z","timestamp":1728245653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20176-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031201752","9783031201769"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20176-9_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ANTS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"antsw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ants2022.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,0222","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,3076","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4 extended abstracts","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}