{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:59:32Z","timestamp":1742921972066,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031366215"},{"type":"electronic","value":"9783031366222"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36622-2_33","type":"book-chapter","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T12:02:36Z","timestamp":1688731356000},"page":"401-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Two-Stage Evolutionary Algorithm with Repair Strategy for Heat Component-Constrained Layout Optimization"],"prefix":"10.1007","author":[{"given":"Ke","family":"Shi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3728-3342","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0643-7134","authenticated-orcid":false,"given":"Xinyue","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5319-7463","authenticated-orcid":false,"given":"Wang","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"33_CR1","unstructured":"Popovic, J., Ferreira, J.A.: Concepts for high packaging and integration efficiency. In: 35th Annual IEEE Power Electronics Specialists Conference (PESC 04), pp. 1\u201313. Springer, Heidelberg (2016)"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Mansouri, N., Weasner, C., Zaghlol, A.: Characterization of a heat sink with embedded heat pipe with variable heat dissipating source placement for power electronics applications. In: 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), CA, San Diego, pp. 311\u2013317 (2018)","DOI":"10.1109\/ITHERM.2018.8419599"},{"issue":"5","key":"33_CR3","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.engstruct.2012.05.013","volume":"43","author":"Z Qiao","year":"2012","unstructured":"Qiao, Z., Zhang, W., Zhu, J., Tong, G.: Layout optimization of multi-component structures under static loads and random excitations. Eng. Struct. 43(5), 120\u2013128 (2012)","journal-title":"Eng. Struct."},{"issue":"1\u20132","key":"33_CR4","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/0004-3702(96)81371-3","volume":"84","author":"D Whitley","year":"1996","unstructured":"Whitley, D., Rana, S., Dzubera, J., Mathias, K.E.: Evaluating evolutionary algorithms. Artif. Intell. 84(1\u20132), 357\u2013358 (1996)","journal-title":"Artif. Intell."},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v053.i04","volume":"53","author":"L Scrucca","year":"2013","unstructured":"Scrucca, L.: GA: a package for genetic algorithms in R. J. Stat. Softw. 53, 1\u201337 (2013)","journal-title":"J. Stat. Softw."},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: IEEE Swarm Intelligence Symposium (2007)","DOI":"10.1109\/SIS.2007.368035"},{"issue":"4","key":"33_CR7","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Kumar, R., Jyotishree: Blending roulette wheel selection & rank selection in genetic algorithms (2012)","DOI":"10.7763\/IJMLC.2012.V2.146"},{"key":"33_CR9","unstructured":"Blickle, T., Thiele, L.: A Mathematical Analysis of Tournament Selection. Morgan Kaufmann Publishers Inc., Burlington (1998)"},{"key":"33_CR10","unstructured":"Deb, K., Beyer, H.G.: Self-Adaptation in Real-Parameter Genetic Algorithms with Simulated Binary Crossover. Morgan Kaufmann Publishers Inc., Burlington (1999)"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Hamdan, M.: A dynamic polynomial mutation for evolutionary multi-objective optimization algorithms. Int. J. Artif. Intell. Tools 20(01), 209\u2013219 (2011)","DOI":"10.1142\/S0218213011000097"},{"key":"33_CR12","unstructured":"Nyawade, K.O.: Generalized inverse Gaussian distributions under different parametrizations research report. Mathematics, Number 27 (2018)"},{"key":"33_CR13","unstructured":"Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report - TR06 (2005)"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Hansen, N., Ostermeier, A.: Completely Derandomized Self-Adaptation in Evolution Strategies. MIT Press, Cambridge (2001)","DOI":"10.1162\/106365601750190398"},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.asoc.2016.11.047","volume":"51","author":"GY Zhu","year":"2017","unstructured":"Zhu, G.Y., Zhang, W.B.: Optimal foraging algorithm for global optimization. Appl. Soft Comput. 51, 294\u2013313 (2017)","journal-title":"Appl. Soft Comput."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 71\u201378. IEEE (2013)","DOI":"10.1109\/CEC.2013.6557555"},{"issue":"25","key":"33_CR17","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TEVC.2020.3004012","volume":"1","author":"Y Tian","year":"2021","unstructured":"Tian, Y., Zhang, T., Xiao, J., Zhang, X., Jin, Y.: A coevolutionary framework for constrained multi-objective optimization problems. IEEE Trans. Evol. Comput. 1(25), 102\u2013116 (2021)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"52","key":"33_CR18","doi-asserted-by":"publisher","first-page":"9559","DOI":"10.1109\/TCYB.2020.3021138","volume":"9","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Zhang, Y., Su, Y., Zhang, X., Tan, K.C., Jin, Y.: Balancing objective optimization and constraint satisfaction in constrained evolutionary multi-objective optimization. IEEE Trans. Cybern. 9(52), 9559\u20139572 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Derrac, J., Garc\u00eda, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3\u201318 (2011)","DOI":"10.1016\/j.swevo.2011.02.002"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Bojan-Dragos, C.A., et al.: GWO-based optimal tuning of type-1 and type-2 fuzzy controllers for electromagnetic actuated clutch systems. In: 4th IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control (CESCIT), Valenciennes, France, pp. 189\u2013194 (2021)","DOI":"10.1016\/j.ifacol.2021.10.032"},{"issue":"14","key":"33_CR21","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.2991\/ijcis.d.210309.001","volume":"1","author":"RE Precup","year":"2021","unstructured":"Precup, R.E., David, R.C., Roman, R.C., Petriu, E.M., Szedlak-Stinean, A.I.: Slime mould algorithm-based tuning of cost-effective fuzzy controllers for servo systems. Int. J. Comput. Intell. Syst. 1(14), 1042\u20131052 (2021)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"33_CR22","doi-asserted-by":"publisher","first-page":"107896","DOI":"10.1016\/j.knosys.2021.107896","volume":"238","author":"ZH Cai","year":"2022","unstructured":"Cai, Z.H., Gao, S.C., Yang, X., Yang, G., Cheng, S., Shi, Y.H.: Alternate search pattern-based brain storm optimization. Knowl.-Based Syst. 238, 107896 (2022)","journal-title":"Knowl.-Based Syst."}],"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\/978-3-031-36622-2_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:18:25Z","timestamp":1710260305000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36622-2_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031366215","9783031366222"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36622-2_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 July 2023","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":"Shenzhen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2023","order":10,"name":"conference_id","label":"Conference ID","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":"170","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":"81","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":"48% - 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":"2.6","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":"3","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)"}}]}}