Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63556499" target="_blank" >RIV/70883521:28140/22:63556499 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/22:10249925 RIV/61989100:27240/22:10249925
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2210650222000293?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650222000293?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.swevo.2022.101057" target="_blank" >10.1016/j.swevo.2022.101057</a>
Alternative languages
Result language
angličtina
Original language name
Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution
Original language description
Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems. © 2022
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Swarm and Evolutionary Computation
ISSN
2210-6502
e-ISSN
2210-6510
Volume of the periodical
71
Issue of the periodical within the volume
Neuveden
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
1-13
UT code for WoS article
000795579900002
EID of the result in the Scopus database
2-s2.0-85129522048