{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T05:42:42Z","timestamp":1770529362613,"version":"3.49.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031453670","type":"print"},{"value":"9783031453687","type":"electronic"}],"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\/978-3-031-45368-7_14","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:37:45Z","timestamp":1697056665000},"page":"209-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-algorithm Approach to\u00a0the\u00a0Optimization of\u00a0Thermal Power Plants Operation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6154-3525","authenticated-orcid":false,"given":"Gabriela T.","family":"Justino","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7365-0130","authenticated-orcid":false,"given":"Gabriela C.","family":"Freitas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5363-4179","authenticated-orcid":false,"given":"Camilla B.","family":"Batista","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4332-5641","authenticated-orcid":false,"given":"Kleyton P.","family":"Cotta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7181-6430","authenticated-orcid":false,"given":"Bruno","family":"Deon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1615-3283","authenticated-orcid":false,"suffix":"Jr.","given":"Fl\u00e1vio L.","family":"Lou\u00e7\u00e3o","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4910-6597","authenticated-orcid":false,"given":"Rodrigo J. S.","family":"de Almeida","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7211-4723","authenticated-orcid":false,"suffix":"Jr.","given":"Carlos A. A.","family":"de Ara\u00fajo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","unstructured":"Al-Amyal, F., Al-attabi, K.J., Al-khayyat, A.: Multistage ant colony algorithm for economic emission dispatch problem. In: 2019 International IEEE Conference and Workshop in \u00d3buda on Electrical and Power Engineering (CANDO-EPE), pp. 161\u2013166 (2019). https:\/\/doi.org\/10.1109\/CANDO-EPE47959.2019.9111048","DOI":"10.1109\/CANDO-EPE47959.2019.9111048"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ijepes.2004.09.004","volume":"27","author":"M Basu","year":"2005","unstructured":"Basu, M.: A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems. Int. J. Electr. Power Energy Syst. 27, 147\u2013153 (2005). https:\/\/doi.org\/10.1016\/j.ijepes.2004.09.004","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Basu, M.: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. Int. J. Electric. Power Energy Syst. 30, 140\u2013149 (2008). https:\/\/doi.org\/10.1016\/j.ijepes.2007.06.009","DOI":"10.1016\/j.ijepes.2007.06.009"},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TPWRS.2002.807115","volume":"18","author":"I Damousis","year":"2003","unstructured":"Damousis, I., Bakirtzis, A., Dokopoulos, P.: Network-constrained economic dispatch using real-coded genetic algorithm. IEEE Trans. Power Syst. 18(1), 198\u2013205 (2003). https:\/\/doi.org\/10.1109\/TPWRS.2002.807115","journal-title":"IEEE Trans. Power Syst."},{"key":"14_CR5","doi-asserted-by":"publisher","unstructured":"Fonseca, M., Bezerra, U.H., Brito, J.D.A., Leite, J.C., Nascimento, M.H.R.: Pre-dispatch of load in thermoelectric power plants considering maintenance management using fuzzy logic. IEEE Access 6, 41379\u201341390 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2854612","DOI":"10.1109\/ACCESS.2018.2854612"},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"Hanafi, I.F., Dalimi, I.R.: Economic load dispatch optimation of thermal power plant based on merit order and bat algorithm. In: 2019 IEEE International Conference on Innovative Research and Development (ICIRD), pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/ICIRD47319.2019.9074734","DOI":"10.1109\/ICIRD47319.2019.9074734"},{"key":"14_CR7","doi-asserted-by":"publisher","unstructured":"Jayabarathi, T., Raghunathan, T., Adarsh, B.R., Suganthan, P.N.: Economic dispatch using hybrid grey wolf optimizer. Energy 111, 630\u2013641 (2016). https:\/\/doi.org\/10.1016\/j.energy.2016.05.105","DOI":"10.1016\/j.energy.2016.05.105"},{"issue":"3\u20134","key":"14_CR8","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1504\/IJCAT.2021.120459","volume":"66","author":"RD Kumar","year":"2021","unstructured":"Kumar, R.D., Chakrapani, A., Kannan, S.: Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for covid-19 research. Int. J. Comput. Appl. Technol. 66(3\u20134), 334\u2013339 (2021). https:\/\/doi.org\/10.1504\/IJCAT.2021.120459","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"14_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116480","volume":"286","author":"H Liu","year":"2021","unstructured":"Liu, H., Shen, X., Guo, Q., Sun, H.: A data-driven approach towards fast economic dispatch in electricity-gas coupled systems based on artificial neural network. Appl. Energy 286, 116480 (2021). https:\/\/doi.org\/10.1016\/j.apenergy.2021.116480","journal-title":"Appl. Energy"},{"issue":"2","key":"14_CR10","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1504\/IJCAT.2022.123466","volume":"68","author":"B Nahavandi","year":"2022","unstructured":"Nahavandi, B., Homayounfar, M., Daneshvar, A., Shokouhifar, M.: Hierarchical structure modelling in uncertain emergency location-routing problem using combined genetic algorithm and simulated annealing. Int. J. Comput. Appl. Technol. 68(2), 150\u2013163 (2022). https:\/\/doi.org\/10.1504\/IJCAT.2022.123466","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s00202-016-0385-2","volume":"99","author":"MHR Nascimento","year":"2017","unstructured":"Nascimento, M.H.R., Nunes, M.V.A., Rodr\u00edguez, J.L.M., Leite, J.C.: A new solution to the economical load dispatch of power plants and optimization using differential evolution. Electr. Eng. 99, 561\u2013571 (2017). https:\/\/doi.org\/10.1007\/s00202-016-0385-2","journal-title":"Electr. Eng."},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"e Silva, M.D.A.C., Klein, C.E., Mariani, V.C., Coelho, L.D.S.: Multiobjective scatter search approach with new combination scheme applied to solve environmental\/economic dispatch problem. Energy 53(C), 14\u201321 (2013). https:\/\/doi.org\/10.1016\/j.energy.2013.02.045","DOI":"10.1016\/j.energy.2013.02.045"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Silva, I.C. Jr., do Nascimento, F.R., de Oliveira, E.J., Marcato, A.L., de Oliveira, L.W., Passos Filho, J.A.: Programming of thermoelectric generation systems based on a heuristic composition of ant colonies. Int. J. Electric. Power Energy Syst. 44, 134\u2013145 (2013). https:\/\/doi.org\/10.1016\/j.ijepes.2012.07.036","DOI":"10.1016\/j.ijepes.2012.07.036"},{"key":"14_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109021","volume":"124","author":"A Sundaram","year":"2022","unstructured":"Sundaram, A.: Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network. Appl. Soft Comput. 124, 109021 (2022). https:\/\/doi.org\/10.1016\/j.asoc.2022.109021","journal-title":"Appl. Soft Comput."},{"key":"14_CR15","doi-asserted-by":"publisher","unstructured":"Tian, J., Wei, H., Tan, J.: Global optimization for power dispatch problems based on theory of moments. Int. J. Electric. Power Energy Syst. 71, 184\u2013194 (2015). https:\/\/doi.org\/10.1016\/j.ijepes.2015.02.018","DOI":"10.1016\/j.ijepes.2015.02.018"},{"key":"14_CR16","doi-asserted-by":"publisher","unstructured":"Wang, K., Zhou, C., Jia, R., He, W.: Adaptive variable weights immune particle swarm optimization for economic dispatch of power system. In: 2020 Asia Energy and Electrical Engineering Symposium (AEEES), pp. 886\u2013891 (2020). https:\/\/doi.org\/10.1109\/AEEES48850.2020.9121455","DOI":"10.1109\/AEEES48850.2020.9121455"},{"issue":"1","key":"14_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJCAT.2022.123234","volume":"68","author":"H Wei","year":"2022","unstructured":"Wei, H., Chen, S., Pan, T., Tao, J., Zhu, M.: Capacity configuration optimisation of hybrid renewable energy system using improved grey wolf optimiser. Int. J. Comput. Appl. Technol. 68(1), 1\u201311 (2022). https:\/\/doi.org\/10.1504\/IJCAT.2022.123234","journal-title":"Int. J. Comput. Appl. Technol."},{"issue":"2\u20133","key":"14_CR18","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1504\/IJCAT.2021.121524","volume":"67","author":"MJ Zhang","year":"2021","unstructured":"Zhang, M.J., Long, D.Y., Li, D.D., Wang, X., Qin, T., Yang, J.: A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation. Int. J. Comput. Appl. Technol. 67(2\u20133), 194\u2013203 (2021). https:\/\/doi.org\/10.1504\/IJCAT.2021.121524","journal-title":"Int. J. Comput. Appl. Technol."}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45368-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:28:49Z","timestamp":1709828929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45368-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453670","9783031453687"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45368-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belo Horizonte","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":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","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":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","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":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","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":"90","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":"37% - 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":"4","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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}