{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:30:38Z","timestamp":1742992238313,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031486487"},{"type":"electronic","value":"9783031486494"}],"license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-48649-4_3","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T06:03:13Z","timestamp":1701410593000},"page":"44-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing Manual vs Automatic Tuning of\u00a0Differential Evolution Strategies for\u00a0Energy Resource Management Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9504-0501","authenticated-orcid":false,"given":"Jos\u00e9","family":"Almeida","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8638-8373","authenticated-orcid":false,"given":"Fernando","family":"Lezama","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4172-4502","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.110607","volume":"137","author":"M Yazdanie","year":"2021","unstructured":"Yazdanie, M., Orehounig, K.: Advancing urban energy system planning and modeling approaches: gaps and solutions in perspective. Renew. Sustain. Energy Rev. 137, 110607 (2021)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"3_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117926","volume":"306","author":"J Milford","year":"2022","unstructured":"Milford, J., Henrion, M., Hunter, C., Newes, E., Hughes, C., Baldwin, S.F.: Energy sector portfolio analysis with uncertainty. Appl. Energy 306, 117926 (2022)","journal-title":"Appl. Energy"},{"doi-asserted-by":"crossref","unstructured":"Hossain, M.A., Pota, H.R., Squartini, S., Abdou, A.F.: Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renew. Energy 136, 746\u2013757 (2019)","key":"3_CR3","DOI":"10.1016\/j.renene.2019.01.005"},{"key":"3_CR4","first-page":"1","volume":"2018","author":"J Soares","year":"2018","unstructured":"Soares, J., Pinto, T., Lezama, F., Morais, H.: Survey on complex optimization and simulation for the new power systems paradigm. Complexity 2018, 1\u201332 (2018)","journal-title":"Complexity"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.apenergy.2019.01.224","volume":"239","author":"Yu Songyuan","year":"2019","unstructured":"Songyuan, Yu., Fang, F., Liu, Y., Liu, J.: Uncertainties of virtual power plant: problems and countermeasures. Appl. Energy 239, 454\u2013470 (2019)","journal-title":"Appl. Energy"},{"issue":"2","key":"3_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.13164\/mendel.2020.2.009","volume":"26","author":"A Kazikova","year":"2020","unstructured":"Kazikova, A., Pluhacek, M., Senkerik, R.: Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison? MENDEL 26(2), 9\u201316 (2020)","journal-title":"MENDEL"},{"doi-asserted-by":"crossref","unstructured":"Vieira, M., Faia, R., Lezama, F., Vale, Z.: A sensitivity analysis of PSO parameters solving the P2P electricity market problem. In: 2022 IEEE Congress on Evolutionary Computation (CEC), pp. 1\u20137, Padua, Italy, July (2022). IEEE","key":"3_CR7","DOI":"10.1109\/CEC55065.2022.9870290"},{"key":"3_CR8","series-title":"International Series in Operations Research & Management Science","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-319-91086-4_17","volume-title":"Handbook of Metaheuristics","author":"T St\u00fctzle","year":"2019","unstructured":"St\u00fctzle, T., L\u00f3pez-Ib\u00e1\u00f1ez, M.: Automated design of metaheuristic algorithms. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. ISORMS, vol. 272, pp. 541\u2013579. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-91086-4_17"},{"key":"3_CR9","first-page":"221","volume":"197","author":"M Birattari","year":"2009","unstructured":"Birattari, M.: Tuning metaheuristics: a machine learning perspective. Tuning Metaheuristics 197, 221 (2009)","journal-title":"Tuning Metaheuristics"},{"unstructured":"Birattari, M., St\u00fctzle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, GECCO 2002, pp. 11\u201318, San Francisco, CA, USA (2002). Morgan Kaufmann Publishers Inc","key":"3_CR10"},{"doi-asserted-by":"crossref","unstructured":"Yuan, Z., St\u00fctzle, T., Montes de Oca, M.A., Lau, H.C., Birattari, M.: An analysis of post-selection in automatic configuration. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference - GECCO 2013, pp. 1557, Amsterdam, The Netherlands (2013). ACM Press","key":"3_CR11","DOI":"10.1145\/2463372.2463562"},{"issue":"1","key":"3_CR12","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1287\/opre.1050.0243","volume":"54","author":"B Adenso-D\u00edaz","year":"2006","unstructured":"Adenso-D\u00edaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Oper. Res. 54(1), 99\u2013114 (2006)","journal-title":"Oper. Res."},{"doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., C\u00e1ceres, L.P., Birattari, M., St\u00fctzle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016)","key":"3_CR13","DOI":"10.1016\/j.orp.2016.09.002"},{"issue":"2","key":"3_CR14","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TEVC.2019.2921598","volume":"24","author":"C Huang","year":"2020","unstructured":"Huang, C., Li, Y., Yao, X.: A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans. Evol. Comput. 24(2), 201\u2013216 (2020)","journal-title":"IEEE Trans. Evol. Comput."},{"doi-asserted-by":"crossref","unstructured":"Lezama, F., Sucar, E., de Cote, E.M., Soares, J., Vale, Z.: Differential evolution strategies for large-scale energy resource management in smart grids. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1279\u20131286, Berlin Germany, July (2017). ACM","key":"3_CR15","DOI":"10.1145\/3067695.3082478"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.swevo.2016.02.005","volume":"29","author":"J Soares","year":"2016","unstructured":"Soares, J., Ghazvini, M.A.F., Silva, M., Vale, Z.: Multi-dimensional signaling method for population-based metaheuristics: solving the large-scale scheduling problem in smart grids. Swarm Evol. Comput. 29, 13\u201332 (2016)","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"3_CR17","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1109\/61.25627","volume":"4","author":"ME Baran","year":"1989","unstructured":"Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4(2), 1401\u20131407 (1989)","journal-title":"IEEE Trans. Power Deliv."},{"issue":"6","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.3390\/en5061881","volume":"5","author":"J Soares","year":"2012","unstructured":"Soares, J., Canizes, B., Lobo, C., Vale, Z., Morais, H.: Electric vehicle scenario simulator tool for smart grid operators. Energies 5(6), 1881\u20131899 (2012)","journal-title":"Energies"}],"container-title":["Lecture Notes in Computer Science","Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48649-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T06:03:32Z","timestamp":1701410612000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48649-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"ISBN":["9783031486487","9783031486494"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48649-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"2 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EI.A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Energy Informatics Academy Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"6 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.energyinformatics.academy\/eia-2023-conference","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","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":"32","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":"7","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":"60% - 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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3 other papers","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)"}}]}}