FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308222718> ?p ?o ?g. }

• W4308222718 endingPage "115652" @default.
• W4308222718 startingPage "115652" @default.
• W4308222718 abstract "Due to the global progress, the optimization problems are becoming more and more complex. Hence, deterministic and heuristic approaches are no longer adequate for dealing with such sophisticated problems. subsequently, metaheuristics have recently emerged as an effective alternative for addressing the optimization problems. This paper proposes a novel metaheuristic called Young’s Double-Slit Experiment (YDSE) optimizer, derived from a physical backdrop. The YDSE optimizer is inspired by Young’s double-slit experiment, which is regarded as one of the most well-known classical physics experiments, revealing the wave nature of light. In YDSE optimizer, each fringe represents a possible solution in the population. Many concepts are modeled from the experiment, such as monochromatic light waves, Huygens’ principle, constructive and destructive interference, wave intensity, amplitude, and path difference. The YDSE optimizer strikes a balance between exploration and exploitation by selecting either a constructive interference or a destructive interference based on the order number of the fringe. During the optimization process, the solution moves in search space based on its order number. If the solution has an odd number, it moves in the dark regions towards the central bright region, which is expected to contain the optimal solution. The algorithm exploits the promising areas in the bright fringe areas, which are assumed to contain the optimum. The performance of the YDSE optimizer is compared with another twelve metaheuristics using CEC 2014, CEC 2017, and CEC 2022. The benchmarks cover different unimodal, multimodal, hybrid, and composite test functions. Also, we consider ten constrained and unconstrained engineering optimization design problems. YDSE proved its superiority over the CEC 2014 and CEC 2017 winners, such as L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA. The results and the statistical analysis demonstrated the outperformance of the proposed YDSE optimizer at a 95% confidence interval. • YDSE optimizer is a novel metaheuristic inspired by Young’s double-slit experiment. • We compare YDSE to existing algorithms using CEC 2014, 2017, 2022 benchmarks. • Ten optimization problems are considered from the engineering field. • The statistical analysis demonstrated the superiority of the proposed YDSE. • YDSE optimizer outperforms L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA." @default.
• W4308222718 created "2022-11-09" @default.
• W4308222718 creator A5032097112 @default.
• W4308222718 creator A5053213515 @default.
• W4308222718 creator A5068555962 @default.
• W4308222718 creator A5079704928 @default.
• W4308222718 date "2023-01-01" @default.
• W4308222718 modified "2023-10-16" @default.
• W4308222718 title "Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems" @default.
• W4308222718 cites W1476700340 @default.
• W4308222718 cites W1595159159 @default.
• W4308222718 cites W1965557865 @default.
• W4308222718 cites W1968370297 @default.
• W4308222718 cites W1974336701 @default.
• W4308222718 cites W1975436849 @default.
• W4308222718 cites W1984762043 @default.
• W4308222718 cites W1987343170 @default.
• W4308222718 cites W1997600725 @default.
• W4308222718 cites W1999284878 @default.
• W4308222718 cites W2024077425 @default.
• W4308222718 cites W2031183907 @default.
• W4308222718 cites W2058443055 @default.
• W4308222718 cites W2061438946 @default.
• W4308222718 cites W2072955302 @default.
• W4308222718 cites W2111986215 @default.
• W4308222718 cites W2119802261 @default.
• W4308222718 cites W2129998174 @default.
• W4308222718 cites W2145126461 @default.
• W4308222718 cites W2151554678 @default.
• W4308222718 cites W2168081761 @default.
• W4308222718 cites W2171074980 @default.
• W4308222718 cites W2232317135 @default.
• W4308222718 cites W2290883490 @default.
• W4308222718 cites W2324628269 @default.
• W4308222718 cites W2405250443 @default.
• W4308222718 cites W2511284938 @default.
• W4308222718 cites W2738900493 @default.
• W4308222718 cites W2770964993 @default.
• W4308222718 cites W2791899797 @default.
• W4308222718 cites W2884377042 @default.
• W4308222718 cites W2944023289 @default.
• W4308222718 cites W2965837582 @default.
• W4308222718 cites W2969430537 @default.
• W4308222718 cites W2976560662 @default.
• W4308222718 cites W2979736100 @default.
• W4308222718 cites W2982291081 @default.
• W4308222718 cites W2985845430 @default.
• W4308222718 cites W2990471907 @default.
• W4308222718 cites W3011104345 @default.
• W4308222718 cites W3012087746 @default.
• W4308222718 cites W3014974411 @default.
• W4308222718 cites W3038102268 @default.
• W4308222718 cites W3046463181 @default.
• W4308222718 cites W3082974932 @default.
• W4308222718 cites W3083171042 @default.
• W4308222718 cites W3088041907 @default.
• W4308222718 cites W3096308392 @default.
• W4308222718 cites W3111635904 @default.
• W4308222718 cites W3116061545 @default.
• W4308222718 cites W3119051141 @default.
• W4308222718 cites W3129058368 @default.
• W4308222718 cites W3139484821 @default.
• W4308222718 cites W3154719286 @default.
• W4308222718 cites W3163903840 @default.
• W4308222718 cites W3168798875 @default.
• W4308222718 cites W3185076117 @default.
• W4308222718 cites W3188136681 @default.
• W4308222718 cites W3193043613 @default.
• W4308222718 cites W3194904024 @default.
• W4308222718 cites W3203480234 @default.
• W4308222718 cites W3205760602 @default.
• W4308222718 cites W3213330460 @default.
• W4308222718 cites W3215664119 @default.
• W4308222718 cites W3217501705 @default.
• W4308222718 cites W4211075681 @default.
• W4308222718 cites W4213080281 @default.
• W4308222718 cites W4213263928 @default.
• W4308222718 cites W4214638371 @default.
• W4308222718 cites W4220930691 @default.
• W4308222718 cites W4220933815 @default.
• W4308222718 cites W4225884645 @default.
• W4308222718 cites W582048769 @default.
• W4308222718 cites W883434633 @default.
• W4308222718 doi "https://doi.org/10.1016/j.cma.2022.115652" @default.
• W4308222718 hasPublicationYear "2023" @default.
• W4308222718 type Work @default.
• W4308222718 citedByCount "8" @default.
• W4308222718 countsByYear W43082227182023 @default.
• W4308222718 crossrefType "journal-article" @default.
• W4308222718 hasAuthorship W4308222718A5032097112 @default.
• W4308222718 hasAuthorship W4308222718A5053213515 @default.
• W4308222718 hasAuthorship W4308222718A5068555962 @default.
• W4308222718 hasAuthorship W4308222718A5079704928 @default.
• W4308222718 hasConcept C109718341 @default.
• W4308222718 hasConcept C111919701 @default.
• W4308222718 hasConcept C11413529 @default.
• W4308222718 hasConcept C121332964 @default.
• W4308222718 hasConcept C126255220 @default.

• next