FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2925796521> ?p ?o ?g. }

• W2925796521 endingPage "574" @default.
• W2925796521 startingPage "552" @default.
• W2925796521 abstract "This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem." @default.
• W2925796521 created "2019-04-11" @default.
• W2925796521 creator A5009284729 @default.
• W2925796521 creator A5024866488 @default.
• W2925796521 creator A5076601327 @default.
• W2925796521 date "2019-04-04" @default.
• W2925796521 modified "2023-09-29" @default.
• W2925796521 title "Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV" @default.
• W2925796521 cites W1966172877 @default.
• W2925796521 cites W1984620536 @default.
• W2925796521 cites W1985985406 @default.
• W2925796521 cites W2016814974 @default.
• W2925796521 cites W2026717679 @default.
• W2925796521 cites W2033630480 @default.
• W2925796521 cites W2036290628 @default.
• W2925796521 cites W2040055824 @default.
• W2925796521 cites W2043730973 @default.
• W2925796521 cites W2047046780 @default.
• W2925796521 cites W2054881170 @default.
• W2925796521 cites W2061074455 @default.
• W2925796521 cites W2073994181 @default.
• W2925796521 cites W2074416932 @default.
• W2925796521 cites W2088078417 @default.
• W2925796521 cites W2090431806 @default.
• W2925796521 cites W2090708786 @default.
• W2925796521 cites W2092372132 @default.
• W2925796521 cites W2123724056 @default.
• W2925796521 cites W2123808725 @default.
• W2925796521 cites W2125595978 @default.
• W2925796521 cites W2126105956 @default.
• W2925796521 cites W2160394370 @default.
• W2925796521 cites W2164770969 @default.
• W2925796521 cites W2165171393 @default.
• W2925796521 cites W2166472741 @default.
• W2925796521 cites W2167662333 @default.
• W2925796521 cites W2221590122 @default.
• W2925796521 cites W2262771719 @default.
• W2925796521 cites W2313033447 @default.
• W2925796521 cites W2324318760 @default.
• W2925796521 cites W2327855249 @default.
• W2925796521 cites W2414607481 @default.
• W2925796521 cites W2558656908 @default.
• W2925796521 cites W2586670930 @default.
• W2925796521 cites W2589836491 @default.
• W2925796521 cites W2592976053 @default.
• W2925796521 cites W2607443619 @default.
• W2925796521 cites W2723527028 @default.
• W2925796521 cites W2750644869 @default.
• W2925796521 cites W2762235289 @default.
• W2925796521 cites W2768119936 @default.
• W2925796521 cites W2791424161 @default.
• W2925796521 cites W2800820017 @default.
• W2925796521 cites W2801729472 @default.
• W2925796521 cites W2898384566 @default.
• W2925796521 cites W2903384698 @default.
• W2925796521 cites W2904473170 @default.
• W2925796521 cites W4300009529 @default.
• W2925796521 cites W4301102910 @default.
• W2925796521 cites W921098652 @default.
• W2925796521 doi "https://doi.org/10.3390/make1020033" @default.
• W2925796521 hasPublicationYear "2019" @default.
• W2925796521 type Work @default.
• W2925796521 sameAs 2925796521 @default.
• W2925796521 citedByCount "6" @default.
• W2925796521 countsByYear W29257965212019 @default.
• W2925796521 countsByYear W29257965212021 @default.
• W2925796521 countsByYear W29257965212022 @default.
• W2925796521 crossrefType "journal-article" @default.
• W2925796521 hasAuthorship W2925796521A5009284729 @default.
• W2925796521 hasAuthorship W2925796521A5024866488 @default.
• W2925796521 hasAuthorship W2925796521A5076601327 @default.
• W2925796521 hasBestOaLocation W29257965211 @default.
• W2925796521 hasConcept C119857082 @default.
• W2925796521 hasConcept C120665830 @default.
• W2925796521 hasConcept C121332964 @default.
• W2925796521 hasConcept C126255220 @default.
• W2925796521 hasConcept C127413603 @default.
• W2925796521 hasConcept C13393347 @default.
• W2925796521 hasConcept C146978453 @default.
• W2925796521 hasConcept C154945302 @default.
• W2925796521 hasConcept C186394612 @default.
• W2925796521 hasConcept C192209626 @default.
• W2925796521 hasConcept C33923547 @default.
• W2925796521 hasConcept C41008148 @default.
• W2925796521 hasConcept C50644808 @default.
• W2925796521 hasConcept C85617194 @default.
• W2925796521 hasConceptScore W2925796521C119857082 @default.
• W2925796521 hasConceptScore W2925796521C120665830 @default.
• W2925796521 hasConceptScore W2925796521C121332964 @default.
• W2925796521 hasConceptScore W2925796521C126255220 @default.
• W2925796521 hasConceptScore W2925796521C127413603 @default.
• W2925796521 hasConceptScore W2925796521C13393347 @default.
• W2925796521 hasConceptScore W2925796521C146978453 @default.
• W2925796521 hasConceptScore W2925796521C154945302 @default.
• W2925796521 hasConceptScore W2925796521C186394612 @default.
• W2925796521 hasConceptScore W2925796521C192209626 @default.
• W2925796521 hasConceptScore W2925796521C33923547 @default.
• W2925796521 hasConceptScore W2925796521C41008148 @default.

• next