FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2949740014> ?p ?o ?g. }

• W2949740014 endingPage "557" @default.
• W2949740014 startingPage "537" @default.
• W2949740014 abstract "Engineering design commonly involves optimization of multiple conflicting performance objectives. During the optimization process, the performance of each candidate design/solution is evaluated using a model which may be empirical, numerical, experimental, etc., among other forms. The accuracy of the underlying model in representing the real-world behavior is referred to as fidelity. A low-fidelity model may be quick to evaluate but not very accurate; whereas a high-fidelity model may be computationally expensive to evaluate but provides an accurate estimate of the true performance. The paradigm of utilizing the low and high-fidelity models’ information to identify the high-fidelity optimal solution(s) is known as multi-fidelity optimization. This study delves into multi-fidelity optimization for problems which contain multiple objectives and where iterative solvers such as finite element analysis, computational fluid dynamics, etc. are used for performance evaluation. By stopping the solver at various stages before convergence, lower-fidelity performance estimates can be obtained at reduced computational cost. Most of the existing multi-fidelity methods can only deal with two fidelities (high and low) and a single objective. To overcome this research gap, we present a novel multi-objective evolutionary algorithm that can deal with multiple (arbitrary) number of fidelities by effectively utilizing pre-converged low-fidelity information. The proposed algorithm uses multiple surrogate models to capture the underlying function(s) with enhanced precision. A decomposition-based scheme is deployed for improved scalability in higher number of objectives. A classifier assisted pre-selection method is used to screen potential non-dominated solutions for efficient use of the computational budget. Additionally, a set of multi-fidelity, multi/many objective benchmark problems with different Pareto front types is also introduced to aid a systematic benchmarking. Numerical experiments are presented to highlight the efficacy of the proposed approach." @default.
• W2949740014 created "2019-06-27" @default.
• W2949740014 creator A5061883146 @default.
• W2949740014 creator A5077555584 @default.
• W2949740014 creator A5091003074 @default.
• W2949740014 date "2019-10-01" @default.
• W2949740014 modified "2023-10-11" @default.
• W2949740014 title "A multiple surrogate assisted multi/many-objective multi-fidelity evolutionary algorithm" @default.
• W2949740014 cites W1595159159 @default.
• W2949740014 cites W1784783396 @default.
• W2949740014 cites W1976184158 @default.
• W2949740014 cites W1977046327 @default.
• W2949740014 cites W1978600984 @default.
• W2949740014 cites W1985927867 @default.
• W2949740014 cites W1989691069 @default.
• W2949740014 cites W1994647844 @default.
• W2949740014 cites W2011174137 @default.
• W2949740014 cites W2018044188 @default.
• W2949740014 cites W2018342164 @default.
• W2949740014 cites W2022485595 @default.
• W2949740014 cites W2024008934 @default.
• W2949740014 cites W2028204622 @default.
• W2949740014 cites W2050252992 @default.
• W2949740014 cites W2055523305 @default.
• W2949740014 cites W2058354584 @default.
• W2949740014 cites W2105245738 @default.
• W2949740014 cites W2111526171 @default.
• W2949740014 cites W2112912151 @default.
• W2949740014 cites W2113442785 @default.
• W2949740014 cites W2149454052 @default.
• W2949740014 cites W2155752967 @default.
• W2949740014 cites W2164488670 @default.
• W2949740014 cites W2167159964 @default.
• W2949740014 cites W2187169370 @default.
• W2949740014 cites W2343601797 @default.
• W2949740014 cites W2348881650 @default.
• W2949740014 cites W2466060750 @default.
• W2949740014 cites W2546299924 @default.
• W2949740014 cites W2599319023 @default.
• W2949740014 cites W2751605210 @default.
• W2949740014 cites W2763090371 @default.
• W2949740014 cites W2772079570 @default.
• W2949740014 cites W2772699637 @default.
• W2949740014 cites W2785722638 @default.
• W2949740014 cites W4246916037 @default.
• W2949740014 doi "https://doi.org/10.1016/j.ins.2019.06.016" @default.
• W2949740014 hasPublicationYear "2019" @default.
• W2949740014 type Work @default.
• W2949740014 sameAs 2949740014 @default.
• W2949740014 citedByCount "13" @default.
• W2949740014 countsByYear W29497400142020 @default.
• W2949740014 countsByYear W29497400142021 @default.
• W2949740014 countsByYear W29497400142023 @default.
• W2949740014 crossrefType "journal-article" @default.
• W2949740014 hasAuthorship W2949740014A5061883146 @default.
• W2949740014 hasAuthorship W2949740014A5077555584 @default.
• W2949740014 hasAuthorship W2949740014A5091003074 @default.
• W2949740014 hasConcept C11413529 @default.
• W2949740014 hasConcept C119857082 @default.
• W2949740014 hasConcept C126255220 @default.
• W2949740014 hasConcept C131675550 @default.
• W2949740014 hasConcept C137836250 @default.
• W2949740014 hasConcept C159149176 @default.
• W2949740014 hasConcept C162324750 @default.
• W2949740014 hasConcept C199360897 @default.
• W2949740014 hasConcept C2776459999 @default.
• W2949740014 hasConcept C2777303404 @default.
• W2949740014 hasConcept C2778770139 @default.
• W2949740014 hasConcept C33923547 @default.
• W2949740014 hasConcept C41008148 @default.
• W2949740014 hasConcept C48044578 @default.
• W2949740014 hasConcept C50522688 @default.
• W2949740014 hasConcept C76155785 @default.
• W2949740014 hasConcept C77088390 @default.
• W2949740014 hasConceptScore W2949740014C11413529 @default.
• W2949740014 hasConceptScore W2949740014C119857082 @default.
• W2949740014 hasConceptScore W2949740014C126255220 @default.
• W2949740014 hasConceptScore W2949740014C131675550 @default.
• W2949740014 hasConceptScore W2949740014C137836250 @default.
• W2949740014 hasConceptScore W2949740014C159149176 @default.
• W2949740014 hasConceptScore W2949740014C162324750 @default.
• W2949740014 hasConceptScore W2949740014C199360897 @default.
• W2949740014 hasConceptScore W2949740014C2776459999 @default.
• W2949740014 hasConceptScore W2949740014C2777303404 @default.
• W2949740014 hasConceptScore W2949740014C2778770139 @default.
• W2949740014 hasConceptScore W2949740014C33923547 @default.
• W2949740014 hasConceptScore W2949740014C41008148 @default.
• W2949740014 hasConceptScore W2949740014C48044578 @default.
• W2949740014 hasConceptScore W2949740014C50522688 @default.
• W2949740014 hasConceptScore W2949740014C76155785 @default.
• W2949740014 hasConceptScore W2949740014C77088390 @default.
• W2949740014 hasLocation W29497400141 @default.
• W2949740014 hasOpenAccess W2949740014 @default.
• W2949740014 hasPrimaryLocation W29497400141 @default.
• W2949740014 hasRelatedWork W1534737284 @default.
• W2949740014 hasRelatedWork W1999564523 @default.
• W2949740014 hasRelatedWork W2006018771 @default.
• W2949740014 hasRelatedWork W2765202572 @default.

• next