Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019091" target="_blank" >RIV/62690094:18470/22:50019091 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/cmc/v72n1/46905" target="_blank" >https://www.techscience.com/cmc/v72n1/46905</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.024736" target="_blank" >10.32604/cmc.2022.024736</a>

Result language

angličtina

Original language name

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Original language description

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer&apos;s shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm&apos;s performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms&apos; performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

Czech name

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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

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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)

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

CMC-Computers, Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Volume of the periodical

72

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

399-416

UT code for WoS article

000763378900024

EID of the result in the Scopus database

2-s2.0-85125854487