A New "Good and Bad Groups-Based Optimizer" for Solving Various Optimization Problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F21%3A50018149" target="_blank" >RIV/62690094:18470/21:50018149 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/11/10/4382" target="_blank" >https://www.mdpi.com/2076-3417/11/10/4382</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app11104382" target="_blank" >10.3390/app11104382</a>

Result language

angličtina

Original language name

A New "Good and Bad Groups-Based Optimizer" for Solving Various Optimization Problems

Original language description

Optimization is the science that presents a solution among the available solutions considering an optimization problem&apos;s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

"Article Number: 4382"

UT code for WoS article

000662517500001

EID of the result in the Scopus database

2-s2.0-85106624515