Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

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>

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

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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