Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges

Popis výsledku v původním jazyce

This paper introduces the Schrödinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrödinger&apos;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&apos;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&apos;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.

Název v anglickém jazyce

Schrödinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges

Popis výsledku anglicky

This paper introduces the Schrödinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrödinger&apos;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&apos;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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

1872-7409

Svazek periodika

328

Číslo periodika v rámci svazku

328

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

50

Strana od-do

"not paged"

Kód UT WoS článku

001658127000002

EID výsledku v databázi Scopus

2-s2.0-105013650223