{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:51:42Z","timestamp":1742979102985,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030787424"},{"type":"electronic","value":"9783030787431"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-78743-1_21","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:07:50Z","timestamp":1625612870000},"page":"232-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two Modified NichePSO Algorithms for\u00a0Multimodal Optimization"],"prefix":"10.1007","author":[{"given":"Tyler","family":"Crane","sequence":"first","affiliation":[]},{"given":"Andries","family":"Engelbrecht","sequence":"additional","affiliation":[]},{"given":"Beatrice","family":"Ombuki-Berman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"issue":"3","key":"21_CR1","first-page":"43","volume":"36","author":"A Ward","year":"1995","unstructured":"Ward, A., Liker, J.K., Cristiano, J.J., Sobek, D.K.: The second toyota paradox: how delaying decisions can make cars faster. Sloan Manag. Rev. 36(3), 43\u201361 (1995)","journal-title":"Sloan Manag. Rev."},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Wong, K.C., Leung, K.S., Wong, M.H.: Protein structure prediction on a lattice model via multimodal optimization techniques. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO), Portland, OR, USA, pp. 155\u2013162 (2010)","DOI":"10.1145\/1830483.1830513"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Rivera, C., Inostroza-Ponta, M., Villalobos-Cid, M.: A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Vi\u00f1a del Mar, pp. 1\u20137 (2020)","DOI":"10.1109\/CIBCB48159.2020.9277700"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Ren, H., Shen, X., Jia, X.: Research on multimodal algorithms for multi-routes planning based on niche techniques. In: 2020 International Conference on Culture-oriented Science & Technology (ICCST), Beijing, China, pp. 203\u2013207 (2020)","DOI":"10.1109\/ICCST50977.2020.00045"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942\u20131948 (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"21_CR6","first-page":"397","volume-title":"Niching Ability of Basic Particle Swarm Optimization Algorithms IEEE Swarm Intelligence Symposium (SIS)","author":"AP Engelbrecht","year":"2005","unstructured":"Engelbrecht, A.P., Masiye, B.S., Pampard, G.: Niching Ability of Basic Particle Swarm Optimization Algorithms IEEE Swarm Intelligence Symposium (SIS), pp. 397\u2013400. Pasadena, CA, USA (2005)"},{"key":"21_CR7","unstructured":"Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahitis, M.N.: Stretching technique for obtaining global minimizers through particle swarm optimization. In: Proceedings of the Particle Swarm Optimization Workshop (2001)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Brits, R., Engelbrecht, A.P., van den Bergh, F.: Solving systems of unconstrained equations using particle swarm optimization. In: IEEE Conference on Systems, Man, and Cybernetics, Yasmine Hammamet, Tunisia, vol. 3, p. 6 (2002)","DOI":"10.1109\/ICSMC.2002.1176019"},{"key":"21_CR9","unstructured":"Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL), Singapore, pp. 692\u2013696 (2002)"},{"key":"21_CR10","unstructured":"Brits, R., Engelbrecht, A.P., van den Bergh, F.: Scalability of niche PSO. Swarm Intelligence Symposium (SIS) (2003)"},{"key":"21_CR11","first-page":"2297","volume-title":"Enhancing the NichePSO","author":"AP Engelbrecht","year":"2007","unstructured":"Engelbrecht, A.P., van Loggerenberg, L.N.H.: Enhancing the NichePSO, pp. 2297\u20132302. IEEE Congress on Evolutionary Computation, Singapore (2007)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Crane, T., Ombuki-Berman, B., Engelbrecht, A.P.: NichePSO and the merging subswarm problem. In: Proceedings 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). Stockholm, Sweden, pp. 17\u201322 (2020)","DOI":"10.1109\/ISCMI51676.2020.9311551"},{"key":"21_CR13","unstructured":"Crane, T.: Analysis of the Niching Particle Swarm Optimization Algorithm M.Sc. Thesis. Brock University, St. Catharines, Canada (2021)"},{"key":"21_CR14","unstructured":"van den Bergh, F.: An Analysis of Particle Swarm Optimizers Ph.D. Dissertation. University of Pretoria, Pretoria, South Africa (2002)"},{"issue":"4","key":"21_CR15","first-page":"341","volume":"105","author":"F van den Bergh","year":"2010","unstructured":"van den Bergh, F., Engelbrecht, A.P.: A convergence proof for the particle swarm optimizer. Fundam. Inf. 105(4), 341\u2013374 (2010)","journal-title":"Fundam. Inf."},{"key":"21_CR16","unstructured":"Thi\u00e9mard, E.: Economic Generation of Low-Discrepancy Sequences with a b-ary Gray Code. Department of Mathematics, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015, Lausanne, Switzerland"},{"key":"21_CR17","unstructured":"Li, X., Engelbrecht, A.P., Epitropakis, M.: benchmark functions for CEC 2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation Machine Learning Group, RMIT University, Melbourne, VIC, Australia, Tech. Rep. (2013)"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78743-1_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T13:45:24Z","timestamp":1725371124000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78743-1_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030787424","9783030787431"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78743-1_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","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":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","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":"104","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":"0","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":"59% - 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":"2,5","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":"4-5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}