Economic dispatch solution using a genetic algorithm based on arithmetic crossover
dc.contributor.author | Yalcinoz T. | |
dc.contributor.author | Altun H. | |
dc.contributor.author | Uzam M. | |
dc.date.accessioned | 2019-08-01T13:38:39Z | |
dc.date.available | 2019-08-01T13:38:39Z | |
dc.date.issued | 2001 | |
dc.department | Niğde ÖHÜ | |
dc.description | Electricidade de Portugal (EDP);Rede Electrica Nacional (REN);ABB - Portugal;IEEE;ERSE | |
dc.description | 2001 IEEE Porto Power Tech Conference -- 10 September 2001 through 13 September 2001 -- Porto -- 89880 | |
dc.description.abstract | In this paper, a new genetic approach based on arithmetic crossover for solving the economic dispatch problem is proposed. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves the quality of the solution. The new genetic approach is compared with an improved Hopfield NN approach (IHN) [1], a fuzzy logic controlled genetic algorithm (FLCGA) [2], an advance engineered-conditioning genetic approach (AECGA) [3] and an advance Hopfield NN approach (AHNN) [4]. © 2001 IEEE. | |
dc.identifier.doi | 10.1109/PTC.2001.964734 | |
dc.identifier.endpage | 156 | |
dc.identifier.isbn | 0780371399 -- 9780780371392 | |
dc.identifier.scopus | 2-s2.0-84861372120 | |
dc.identifier.scopusquality | N/A | |
dc.identifier.startpage | 153 | |
dc.identifier.uri | https://dx.doi.org/10.1109/PTC.2001.964734 | |
dc.identifier.uri | https://hdl.handle.net/11480/1374 | |
dc.identifier.volume | 2 | |
dc.indekslendigikaynak | Scopus | |
dc.institutionauthor | [0-Belirlenecek] | |
dc.language.iso | en | |
dc.relation.ispartof | 2001 IEEE Porto Power Tech Proceedings | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Arithmetic crossover | |
dc.subject | Economic dispatch | |
dc.subject | Genetic algorithm | |
dc.title | Economic dispatch solution using a genetic algorithm based on arithmetic crossover | |
dc.type | Conference Object |