Matches in SemOpenAlex for { <https://semopenalex.org/work/W2320179423> ?p ?o ?g. }
- W2320179423 endingPage "605" @default.
- W2320179423 startingPage "589" @default.
- W2320179423 abstract "AbstractThis article proposes a simple opposition-based greedy heuristic search to solve a dynamic thermal power dispatch problem as a non-linear constrained optimization problem in the constrained search space. Opposition-based learning is applied at two stages. First, an initial population is generated to select good candidates by extensively exploring the search space. Second, it is implemented for migration to maintain diversity in the set of feasible solutions. The proposed method applies a mutation strategy by perturbing the genes heuristically and seeking a better one, which introduces parallelism and makes the algorithm greedy for a better solution. The greediness and randomness pulls the algorithm toward a global solution. Acceleration of the algorithm is independent of any parameter tuning. Feasible solutions are achieved heuristically by modifying the generation schedules within operating generation limits. Opposition-based greedy heuristic search has been implemented to analyze dynamic economi..." @default.
- W2320179423 created "2016-06-24" @default.
- W2320179423 creator A5034840428 @default.
- W2320179423 creator A5069696874 @default.
- W2320179423 date "2016-03-25" @default.
- W2320179423 modified "2023-09-24" @default.
- W2320179423 title "A Simple Opposition-based Greedy Heuristic Search for Dynamic Economic Thermal Power Dispatch" @default.
- W2320179423 cites W1486897263 @default.
- W2320179423 cites W1968333060 @default.
- W2320179423 cites W1968520181 @default.
- W2320179423 cites W1969413772 @default.
- W2320179423 cites W1973788460 @default.
- W2320179423 cites W1996568027 @default.
- W2320179423 cites W2001658805 @default.
- W2320179423 cites W2005634825 @default.
- W2320179423 cites W2007111112 @default.
- W2320179423 cites W2009162968 @default.
- W2320179423 cites W2010170697 @default.
- W2320179423 cites W2017891670 @default.
- W2320179423 cites W2024432904 @default.
- W2320179423 cites W2024987017 @default.
- W2320179423 cites W2025425272 @default.
- W2320179423 cites W2028629364 @default.
- W2320179423 cites W2030946069 @default.
- W2320179423 cites W2031545514 @default.
- W2320179423 cites W2032092969 @default.
- W2320179423 cites W2035489984 @default.
- W2320179423 cites W2035699466 @default.
- W2320179423 cites W2036426366 @default.
- W2320179423 cites W2046080651 @default.
- W2320179423 cites W2046508941 @default.
- W2320179423 cites W2047878183 @default.
- W2320179423 cites W2056649707 @default.
- W2320179423 cites W2057652411 @default.
- W2320179423 cites W2059167470 @default.
- W2320179423 cites W2060834770 @default.
- W2320179423 cites W2070257229 @default.
- W2320179423 cites W2070709061 @default.
- W2320179423 cites W2072469309 @default.
- W2320179423 cites W2092031071 @default.
- W2320179423 cites W2093704466 @default.
- W2320179423 cites W2097722519 @default.
- W2320179423 cites W2107493209 @default.
- W2320179423 cites W2107757594 @default.
- W2320179423 cites W2109503305 @default.
- W2320179423 cites W2144555430 @default.
- W2320179423 cites W2145228555 @default.
- W2320179423 cites W2167601983 @default.
- W2320179423 cites W4241358655 @default.
- W2320179423 doi "https://doi.org/10.1080/15325008.2015.1122113" @default.
- W2320179423 hasPublicationYear "2016" @default.
- W2320179423 type Work @default.
- W2320179423 sameAs 2320179423 @default.
- W2320179423 citedByCount "9" @default.
- W2320179423 countsByYear W23201794232018 @default.
- W2320179423 countsByYear W23201794232019 @default.
- W2320179423 countsByYear W23201794232020 @default.
- W2320179423 countsByYear W23201794232022 @default.
- W2320179423 crossrefType "journal-article" @default.
- W2320179423 hasAuthorship W2320179423A5034840428 @default.
- W2320179423 hasAuthorship W2320179423A5069696874 @default.
- W2320179423 hasConcept C111472728 @default.
- W2320179423 hasConcept C11413529 @default.
- W2320179423 hasConcept C121332964 @default.
- W2320179423 hasConcept C126255220 @default.
- W2320179423 hasConcept C138885662 @default.
- W2320179423 hasConcept C163258240 @default.
- W2320179423 hasConcept C173801870 @default.
- W2320179423 hasConcept C17744445 @default.
- W2320179423 hasConcept C187633118 @default.
- W2320179423 hasConcept C199539241 @default.
- W2320179423 hasConcept C2780586882 @default.
- W2320179423 hasConcept C2780668109 @default.
- W2320179423 hasConcept C33923547 @default.
- W2320179423 hasConcept C41008148 @default.
- W2320179423 hasConcept C51823790 @default.
- W2320179423 hasConcept C62520636 @default.
- W2320179423 hasConcept C89227174 @default.
- W2320179423 hasConcept C94625758 @default.
- W2320179423 hasConceptScore W2320179423C111472728 @default.
- W2320179423 hasConceptScore W2320179423C11413529 @default.
- W2320179423 hasConceptScore W2320179423C121332964 @default.
- W2320179423 hasConceptScore W2320179423C126255220 @default.
- W2320179423 hasConceptScore W2320179423C138885662 @default.
- W2320179423 hasConceptScore W2320179423C163258240 @default.
- W2320179423 hasConceptScore W2320179423C173801870 @default.
- W2320179423 hasConceptScore W2320179423C17744445 @default.
- W2320179423 hasConceptScore W2320179423C187633118 @default.
- W2320179423 hasConceptScore W2320179423C199539241 @default.
- W2320179423 hasConceptScore W2320179423C2780586882 @default.
- W2320179423 hasConceptScore W2320179423C2780668109 @default.
- W2320179423 hasConceptScore W2320179423C33923547 @default.
- W2320179423 hasConceptScore W2320179423C41008148 @default.
- W2320179423 hasConceptScore W2320179423C51823790 @default.
- W2320179423 hasConceptScore W2320179423C62520636 @default.
- W2320179423 hasConceptScore W2320179423C89227174 @default.
- W2320179423 hasConceptScore W2320179423C94625758 @default.
- W2320179423 hasIssue "6" @default.