Multiscale quantum harmonic oscillator optimization algorithm with multiple quantum perturbations for numerical optimization

Multi-scale quantum harmonic oscillator algorithm (MQHOA) is a recent proposed intelligent algorithm, in which the optimization process can be regarded as the multi-scale quantum annealing process with respect to the constraint of the harmonic oscillator potential well. It has been proved effective...

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Veröffentlicht in:Expert systems with applications Jg. 185; S. 115615
Hauptverfasser: Xin, Gang, Wang, Peng, Jiao, Yuwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.12.2021
Elsevier BV
Schlagworte:
Algorithms
Harmonic oscillators
Monte Carlo simulation
Multiple quantum perturbations
Numerical optimization
Optimization
Perturbation
Quantum-inspired algorithm
Sampling
Time-dependent Schrödinger equation
Multiple quantum perturbations
Time-dependent Schrödinger equation
Numerical optimization
Quantum-inspired algorithm
ISSN:0957-4174, 1873-6793
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Zusammenfassung:Multi-scale quantum harmonic oscillator algorithm (MQHOA) is a recent proposed intelligent algorithm, in which the optimization process can be regarded as the multi-scale quantum annealing process with respect to the constraint of the harmonic oscillator potential well. It has been proved effective and efficient to deal with unimodal and multimodal numerical optimization problems. However, it takes a long time for the particles system to reach the ground-state equilibrium at each annealing scale. Motivated by this situation, a diffusion Monte Carlo method based dynamic sampling regulation strategy is proposed to enhances the sampling efficiency by dynamically adjusting the sampling frequency. Moreover, the particles with different kinetic energies are introduced into the optimization system as quantum perturbations, which reduces the probability of the algorithm falling into a local optimum by keeping the different searching scales simultaneously. The theoretical derivations of this method are presented. The effectiveness of the algorithm is studied by comparing it with previous versions of the MQHOA and several famous intelligent algorithms on uni- and multimodal benchmark functions. The experimental results illustrate that the proposed method has a comparable performance for numerical optimization problems. •An improved diffusion Monte Carlo process with less computational cost is proposed.•The multiple perturbations are introduced to improve the Monte Carlo process.•The efficiency of annealing process has been greatly improved.
Bibliographie:ObjectType-Article-1
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115615