SSABA: Search Step Adjustment Based Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50020026" target="_blank" >RIV/62690094:18470/22:50020026 - isvavai.cz</a>
Result on the web
<a href="https://www.techscience.com/cmc/v71n3/46515" target="_blank" >https://www.techscience.com/cmc/v71n3/46515</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32604/cmc.2022.023682" target="_blank" >10.32604/cmc.2022.023682</a>
Alternative languages
Result language
angličtina
Original language name
SSABA: Search Step Adjustment Based Algorithm
Original language description
Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.
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
CMC-Computers, Materials & Continua
ISSN
1546-2218
e-ISSN
1546-2226
Volume of the periodical
71
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
4237-4256
UT code for WoS article
000770817300004
EID of the result in the Scopus database
2-s2.0-85122769931