Self-Adaptive Spherical Search With a Low-Precision Projection Matrix for Real-World Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248872" target="_blank" >RIV/61989100:27240/21:10248872 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9646531" target="_blank" >https://ieeexplore.ieee.org/document/9646531</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCYB.2021.3119386" target="_blank" >10.1109/TCYB.2021.3119386</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-Adaptive Spherical Search With a Low-Precision Projection Matrix for Real-World Optimization

Popis výsledku v původním jazyce

Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently developed spherical search (SS) algorithm utilizing the HS search model has been shown to perform very well for the bound-constrained and constrained optimization problems compared to several state-of-the-art algorithms. Nevertheless, the computational burdens for generating an HS locus are higher than that for an HC locus. We propose an efficient technique to construct an HS locus by approximating the orthogonal projection matrix to resolve this issue. As per our empirical experiments, this technique significantly improves the performance of the original SS with less computational effort. Moreover, to enhance SS&apos;s search capability, we put forth a self-adaptation technique for choosing the effective values of the control parameters dynamically during the optimization process. We validate the proposed algorithm&apos;s performance on a plethora of real-world and benchmark optimization problems with and without constraints. Experimental results suggest that the proposed algorithm remains better than or at least comparable to the best-known state-of-the-art algorithms on a wide spectrum of problems.

Název v anglickém jazyce

Self-Adaptive Spherical Search With a Low-Precision Projection Matrix for Real-World Optimization

Popis výsledku anglicky

Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently developed spherical search (SS) algorithm utilizing the HS search model has been shown to perform very well for the bound-constrained and constrained optimization problems compared to several state-of-the-art algorithms. Nevertheless, the computational burdens for generating an HS locus are higher than that for an HC locus. We propose an efficient technique to construct an HS locus by approximating the orthogonal projection matrix to resolve this issue. As per our empirical experiments, this technique significantly improves the performance of the original SS with less computational effort. Moreover, to enhance SS&apos;s search capability, we put forth a self-adaptation technique for choosing the effective values of the control parameters dynamically during the optimization process. We validate the proposed algorithm&apos;s performance on a plethora of real-world and benchmark optimization problems with and without constraints. Experimental results suggest that the proposed algorithm remains better than or at least comparable to the best-known state-of-the-art algorithms on a wide spectrum of problems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/LTAIN19176" target="_blank" >LTAIN19176: Metaheuristický rámec pro vícecílové kombinatorické optimalizační problémy (META MO-COP)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Cybernetics

ISSN

2168-2267

e-ISSN

2168-2275

Svazek periodika

nezaveden

Číslo periodika v rámci svazku

prosinec 2021

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

nestrankovano

Kód UT WoS článku

000732877100001

EID výsledku v databázi Scopus

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