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RWS-L-SHADE :
RWS-L-SHADE : An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation
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- Mousavirad, S. J. (författare)
- Computer Engineering Department, Hakim Sabzevari University, Sabzevar, Iran
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- Helali Moghadam, Mahshid (författare)
- RISE,Mälardalens universitet,RISE Research Institutes of Sweden, Västerås, Sweden,Industriella system,Mälardalen University, Sweden
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- Saadatmand, Mehrdad, 1980- (författare)
- Mälardalens universitet,Inbyggda system,Mälardalen University, Sweden
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- Chakrabortty, R. (författare)
- School of Engineering and Information Technology, UNSW Canberra at ADFA, Canberra, Australia
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- Schaefer, G. (författare)
- Department of Computer Science, Loughborough University, Loughborough, United Kingdom
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- Oliva, D. (författare)
- Depto. de Innovacion Basada en la Informacion y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Mexico,University of Guadalajara, Mexico
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(creator_code:org_t)
- 2022-04-15
- 2022
- Engelska.
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Ingår i: Lecture Notes in Computer Science, vol. 13324. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031024610 ; , s. 255-268
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Farkostteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)
Nyckelord
- CEC-2017 benchmark functions
- Differential evolution
- L-SHADE algorithm
- Optimisation
- Roulette wheel selection strategy
- Global optimization
- Wheels
- Benchmark functions
- CEC-2017 benchmark function
- Differential evolution algorithms
- Global optimization problems
- Numerical optimizations
- Optimisations
- Roulette-wheel selections
- State of the art
- Population statistics
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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