FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2904997552> ?p ?o ?g. }

• W2904997552 endingPage "2287" @default.
• W2904997552 startingPage "2274" @default.
• W2904997552 abstract "Over the last few decades, the decomposition-based multiobjective evolutionary algorithms (DMOEAs) have became one of the mainstreams for multiobjective optimization. However, there is not too much research on applying DMOEAs to uncertain problems until now. Usually, the uncertainty is modeled as additive noise in the objective space, which is the case this paper concentrates on. This paper first carries out experiments to examine the impact of noisy environments on DMOEAs. Then, four noise-handling techniques based upon the analyses of empirical results are proposed. First, a Pareto-based nadir point estimation strategy is put forward to provide a good normalization of each objective. Next, we introduce two adaptive sampling strategies that vary the number of samples used per solution based on the differences among neighboring solutions and their variance to control the tradeoff between exploration and exploitation. Finally, a mixed objective evaluation strategy and a mixed repair mechanism are proposed to alleviate the effects of noise and remedy the loss of diversity in the decision space, respectively. These features are embedded in two popular DMOEAs (i.e., MOEA/D and DMOEA- <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${varepsilon }text{C}$ </tex-math></inline-formula> ), and DMOEAs with these features are called noise-tolerant DMOEAs (NT-DMOEAs). NT-DMOEAs are compared with their various variants and four noise-tolerant multiobjective algorithms, including the improved NSGA-II, the classical algorithm Bayesian (1+1)-ES (BES), and the state-of-the-art algorithms MOP-EA and rolling tide evolutionary algorithm to show the superiority of proposed features on 17 benchmark problems with different strength levels of noise. Experimental studies demonstrate that two NT-DMOEAs, especially NT-DMOEA- <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${varepsilon }text{C}$ </tex-math></inline-formula> , show remarkable advantages over competitors in the majority of test instances." @default.
• W2904997552 created "2018-12-22" @default.
• W2904997552 creator A5010479652 @default.
• W2904997552 creator A5020307082 @default.
• W2904997552 creator A5032906949 @default.
• W2904997552 creator A5050062467 @default.
• W2904997552 date "2020-05-01" @default.
• W2904997552 modified "2023-09-25" @default.
• W2904997552 title "Noise-Tolerant Techniques for Decomposition-Based Multiobjective Evolutionary Algorithms" @default.
• W2904997552 cites W1510354679 @default.
• W2904997552 cites W1550220524 @default.
• W2904997552 cites W1647513660 @default.
• W2904997552 cites W1662894842 @default.
• W2904997552 cites W1846834309 @default.
• W2904997552 cites W1975906723 @default.
• W2904997552 cites W1976159118 @default.
• W2904997552 cites W1976442029 @default.
• W2904997552 cites W1983519058 @default.
• W2904997552 cites W1998411112 @default.
• W2904997552 cites W2000315765 @default.
• W2904997552 cites W2023685757 @default.
• W2904997552 cites W2053495495 @default.
• W2904997552 cites W2078848072 @default.
• W2904997552 cites W2102625537 @default.
• W2904997552 cites W2106334424 @default.
• W2904997552 cites W2109906979 @default.
• W2904997552 cites W2125502051 @default.
• W2904997552 cites W2125899728 @default.
• W2904997552 cites W2126105956 @default.
• W2904997552 cites W2131847304 @default.
• W2904997552 cites W2143381319 @default.
• W2904997552 cites W2147525584 @default.
• W2904997552 cites W2150113394 @default.
• W2904997552 cites W2152043436 @default.
• W2904997552 cites W2154022671 @default.
• W2904997552 cites W2156106639 @default.
• W2904997552 cites W2164691489 @default.
• W2904997552 cites W2604540094 @default.
• W2904997552 cites W2767483349 @default.
• W2904997552 cites W4231591057 @default.
• W2904997552 cites W847164865 @default.
• W2904997552 doi "https://doi.org/10.1109/tcyb.2018.2881227" @default.
• W2904997552 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30530345" @default.
• W2904997552 hasPublicationYear "2020" @default.
• W2904997552 type Work @default.
• W2904997552 sameAs 2904997552 @default.
• W2904997552 citedByCount "7" @default.
• W2904997552 countsByYear W29049975522021 @default.
• W2904997552 countsByYear W29049975522022 @default.
• W2904997552 countsByYear W29049975522023 @default.
• W2904997552 crossrefType "journal-article" @default.
• W2904997552 hasAuthorship W2904997552A5010479652 @default.
• W2904997552 hasAuthorship W2904997552A5020307082 @default.
• W2904997552 hasAuthorship W2904997552A5032906949 @default.
• W2904997552 hasAuthorship W2904997552A5050062467 @default.
• W2904997552 hasConcept C11413529 @default.
• W2904997552 hasConcept C115961682 @default.
• W2904997552 hasConcept C118615104 @default.
• W2904997552 hasConcept C124681953 @default.
• W2904997552 hasConcept C126255220 @default.
• W2904997552 hasConcept C136886441 @default.
• W2904997552 hasConcept C137635306 @default.
• W2904997552 hasConcept C144024400 @default.
• W2904997552 hasConcept C154945302 @default.
• W2904997552 hasConcept C159149176 @default.
• W2904997552 hasConcept C18903297 @default.
• W2904997552 hasConcept C19165224 @default.
• W2904997552 hasConcept C2780069185 @default.
• W2904997552 hasConcept C33923547 @default.
• W2904997552 hasConcept C41008148 @default.
• W2904997552 hasConcept C45357846 @default.
• W2904997552 hasConcept C68781425 @default.
• W2904997552 hasConcept C86803240 @default.
• W2904997552 hasConcept C94375191 @default.
• W2904997552 hasConcept C99498987 @default.
• W2904997552 hasConceptScore W2904997552C11413529 @default.
• W2904997552 hasConceptScore W2904997552C115961682 @default.
• W2904997552 hasConceptScore W2904997552C118615104 @default.
• W2904997552 hasConceptScore W2904997552C124681953 @default.
• W2904997552 hasConceptScore W2904997552C126255220 @default.
• W2904997552 hasConceptScore W2904997552C136886441 @default.
• W2904997552 hasConceptScore W2904997552C137635306 @default.
• W2904997552 hasConceptScore W2904997552C144024400 @default.
• W2904997552 hasConceptScore W2904997552C154945302 @default.
• W2904997552 hasConceptScore W2904997552C159149176 @default.
• W2904997552 hasConceptScore W2904997552C18903297 @default.
• W2904997552 hasConceptScore W2904997552C19165224 @default.
• W2904997552 hasConceptScore W2904997552C2780069185 @default.
• W2904997552 hasConceptScore W2904997552C33923547 @default.
• W2904997552 hasConceptScore W2904997552C41008148 @default.
• W2904997552 hasConceptScore W2904997552C45357846 @default.
• W2904997552 hasConceptScore W2904997552C68781425 @default.
• W2904997552 hasConceptScore W2904997552C86803240 @default.
• W2904997552 hasConceptScore W2904997552C94375191 @default.
• W2904997552 hasConceptScore W2904997552C99498987 @default.
• W2904997552 hasFunder F4320310009 @default.
• W2904997552 hasFunder F4320321001 @default.
• W2904997552 hasFunder F4320322725 @default.

• next