A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019098" target="_blank" >RIV/62690094:18470/22:50019098 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-910/#" target="_blank" >https://peerj.com/articles/cs-910/#</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.910" target="_blank" >10.7717/peerj-cs.910</a>
Alternative languages
Result language
angličtina
Original language name
A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems
Original language description
In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
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
PeerJ Computer Science
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
8
Issue of the periodical within the volume
March
Country of publishing house
GB - UNITED KINGDOM
Number of pages
29
Pages from-to
"Article Number: e910"
UT code for WoS article
000768601100001
EID of the result in the Scopus database
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