Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258438" target="_blank" >RIV/61989100:27240/25:10258438 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0950705125013140?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705125013140?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.knosys.2025.114273" target="_blank" >10.1016/j.knosys.2025.114273</a>
Alternative languages
Result language
angličtina
Original language name
Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges
Original language description
This paper introduces the Schrödinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrödinger's equation and wave-particle duality. SRA possesses a twin update mechanism that balances probabilistic exploration and deterministic exploitation, facilitating effective navigation in high-dimensional, intricate search spaces. The algorithm was extensively tested on benchmark suites such as CEC 2019 (low-dimensional), CEC 2017 (50D and 100D), CEC 2022 (20D), and eight real-world engineering design optimization problems. Comparison tests with state-of-the-art physics-inspired and advanced metaheuristic algorithms revealed SRA's superior performance. In the 100D CEC 2017 benchmark, SRA ranked the best average rank (1.87) among the physics-based algorithms and performed better than its rivals on 20 of the 29 functions. It also performed best (2.92) among emerging metaheuristic variants. Statistical tests (Friedman and Wilcoxon signed rank) confirmed the significance of these results. In engineering applications, SRA consistently obtained better solutions with fewer computations. These findings accentuate SRA's potential in solving complex optimization problems efficiently. This study opens up new possibilities for powerful and versatile optimization methods through the integration of quantum-inspired concepts into the metaheuristic paradigm. The source code is available at https://github.com/MohammedQaraad/SRA/blob/main/SRA_framework.ipynb © 2025 Elsevier B.V., All rights reserved.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Knowledge-Based Systems
ISSN
0950-7051
e-ISSN
1872-7409
Volume of the periodical
328
Issue of the periodical within the volume
328
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
50
Pages from-to
"not paged"
UT code for WoS article
001658127000002
EID of the result in the Scopus database
2-s2.0-105013650223