Bare-bones multi-scale quantum harmonic oscillator algorithm for global optimization

The Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) is a novel heuristic optimization technique, underpinned by the quantum wave function as its theoretical foundation. In this study, we present a streamlined MQHOA framework, dubbed the Bare-Bones Multi-scale Quantum Harmonic Oscillator Al...

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Vydáno v:Expert systems with applications Ročník 238; s. 121870
Hlavní autoři: Guo, Benjun, Jin, Jin, Xu, Yuanping, Zhang, Chaolong, Kong, Chao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2024
Témata:
Bare-bones multi-scale quantum harmonic oscillator algorithm
Global optimization
Heuristic algorithm
Heuristic algorithm
Global optimization
Bare-bones multi-scale quantum harmonic oscillator algorithm
ISSN:0957-4174, 1873-6793
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Shrnutí:The Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) is a novel heuristic optimization technique, underpinned by the quantum wave function as its theoretical foundation. In this study, we present a streamlined MQHOA framework, dubbed the Bare-Bones Multi-scale Quantum Harmonic Oscillator Algorithm (BBMQHOA). This minimalist paradigm employs the most rudimentary free particle in quantum mechanics to simulate particle movement. BBMQHOA leverages group stability over individual stability, enabling the algorithm to fully capitalize on population data, thereby enhancing exploration and exploitation. An extensive array of experiments is carried out on a collection of 24 benchmark functions and a pair of real-world applications to validate the efficacy of our suggested methodology. The outcomes reveal that its performance is both practical and competitive while simultaneously maintaining user-friendliness. •The algorithm gives a probability interpretation of the optimal solution location from a novel point of view.•Establishes a new model based on random sampling and probability model.•BBMQHOA can fully utilize population information to improve exploration and exploitation.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.121870