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Multiscale quantum Harmonic Oscillator algorithm With Multi-Harmonic Oscillators for Numerical Optimization

IEEE ACCESS 2019年 7卷 51159-51170页

作者： Li, Bo Wang, Peng Chinese Acad Sci Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China Univ Chinese Acad Sci Chengdu Inst Comp Beijing 100049 Peoples R China Tangshan Univ Coll Comp Sci & Technol Tangshan 063000 Peoples R China Southwest Minzu Univ Coll Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China

The multiscale quantum harmonic oscillator algorithm (MQHOA) is inspired by the physical meaning of quantum wavefunction. Particles have no efficient interaction and share a wavefunction during the evolution process. In this paper, a multi-harmonic oscillator strategy is introduced, which employs multiple wavefunctions to generate new particles. The external population information can be utilized in the process of evolution. The proposed method enhances the cooperation and interaction for particles by a local collaborative operation. Moreover, an adaptive weight operator is employed in the proposed algorithm, which makes a fine-tune of solutions to keep the diversity of particles. The proposed algorithm is verified on standard benchmark functions. Wilcoxon rank sum test is adopted to ascertain the superiority of the proposed algorithm. The experiments have been conducted with several renowned heuristic algorithms. The numerical results reveal that the proposed algorithm outperforms the comparison algorithms for numerical optimization.

关键词： Multiscale quantum harmonic oscillator algorithm multi-harmonic oscillators wavefunction quantum-inspired algorithm cooperation and interaction

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quantum-inspired Support Vector Machine

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7210-7222页

作者： Ding, Chen Bao, Tian-Yi Huang, He-Liang Univ Sci & Technol China CAS Ctr Excellence & Synerget Innovat Ctr Quantum Hefei 230026 Peoples R China Univ Oxford Dept Comp Sci Oxford OX1 3QD England Univ Sci & Technol China Hefei Natl Lab Phys Sci Ences Microscale Hefei 230026 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Dept Modern Phys Hefei 230026 Peoples R China

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, an improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to nonlinear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number, and high dimensional data matrix, matching the runtime of the quantum SVM.

关键词： Support vector machines Kernel Machine learning algorithms quantum mechanics Machine learning Runtime Optimization Exponential speedup machine learning matrix sampling quantum-inspired algorithm support vector machine (SVM)

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A Novel Portfolio Optimization Model Based on Trend Ratio and Evolutionary Computation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2019年第4期3卷 337-350页

作者： Chou, Yao-Hsin Kuo, Shu-Yu Jiang, Yu-Chi Natl Chi Nan Univ Dept Comp Sci & Informat Engn Nantou 54561 Taiwan

Stock selection is a paramount importance in stock investment. Selecting stocks generally requires considering return and risk simultaneously. The common assessment strategy is to use the Sharpe ratio. Sharpe ratio uses the standard deviation to determine portfolio risk but a portfolio with an uptrend has high risk, defying the logic of most investors. Therefore, this paper proposes a novel assessment strategy, trend ratio. The trend ratio uses linear regression, which is improved with the initial funds when seeking to find the trend of a portfolio. The slope of the trend line is daily expected return, and the difference between the trend line and the funds standardization of a portfolio is daily risk. The trend ratio not only can find the portfolio that has a stable uptrend, but also solves the problem of the Sharpe ratio when a portfolio with an uptrend has high risk. This paper does not limit the number of stocks in a portfolio;it uses the quantum-inspired tabu search algorithm, which is improved by an adaptive strategy, the current best-known solution, and the quantum not gate (ANQTS) to find the best portfolio in a large solution space. This paper employs the sliding window to avoid the over-fitting problem. We use the traditional sliding window, which uses the previous period as training data, as well as the year-on-year sliding window, which uses the same period in the last year as the training data. In summary, this paper combines the trend ratio, ANQTS, and the sliding window to solve the problem of stock selection. The experimental results show that our method finds better portfolios than the Sharpe ratio. Moreover, the year-on-year sliding window, as proposed herein, has promising performance in investment and finds out some suitable economic cycle lengths for stock investment.

关键词： Stock selection portfolio Sharpe ratio linear regression trend ratio evolutionary algorithm sliding windows quantum-inspired algorithm

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A Taxonomy and Survey on Energy-Aware Scientific Workflows Scheduling in Large-Scale Heterogeneous Architecture 6

A Taxonomy and Survey on Energy-Aware Scientific Workflows S...

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6th International Conference on Inventive Computation Technologies (ICICT)

作者： Saurav, Sumit Kumar Benedict, Shajulin Indian Inst Informat Technol Kottayam Ctr Dev Adv Comp Bangalore Karnataka India Indian Inst Informat Technol Kottayam Bangalore Karnataka India

ISBN: (纸本)9781728185019

Power and energy consumption are the primary concerns for large-scale heterogeneous computing systems such as HPC systems and clouds. Power is an identified limiter in the viability and sustainability of exascale systems. To achieve this, we need to improve energy efficiency at all levels of the HPC ecosystem. Scientific workflows are well-known computing models for executing computational and data-intensive workloads on parallel and distributed systems. Energy consumption of scientific workflows on the heterogeneous computing platform is of paramount concern. The workflow management system needs to consider various multiobjective optimization parameters while scheduling and executing scientific workflows. There is a need for a comprehensive and efficient energy-aware workflows runtime system to incorporate energy-aware mechanisms at all levels. This paper has outlined scientific workflows and discussed the distinct challenges in the path of its energy-aware execution. We have discussed the multiobjective optimization problem and presented a survey on the state-of-the-art workflow scheduling algorithms. We have also outlined the need for energy-aware runtime systems and proposed a reference architecture and runtime for energy-aware scientific workflows.

关键词： scientific workflows energy efficiency work flow scheduling multiobjective optimization (MOO) HPC quantum-inspired algorithm reinforcement learning metaheuristic

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Describing quantum-inspired Linear Genetic Programming from Symbolic Regression Problems

Describing Quantum-Inspired Linear Genetic Programming from ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Dias, Douglas Mota Pacheco, Marco Aurelio C. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro RJ Brazil

ISBN: (纸本)9781467315098

quantum-inspired evolutionary algorithms (QIEAs) exploit principles of quantum mechanics to improve the performance of classical evolutionary algorithms. This paper describes the latest version of a QIEA model ("quantum-inspired Linear Genetic Programming" - QILGP) to evolve machine code programs. QILGP is inspired on multilevel quantum systems and its operation is based on quantum individuals, which represent a superposition of all programs of search space (solutions). Symbolic regression problems and the current more efficient model to evolve machine code (AIMGP) are used in comparative tests, which aim to evaluate the performance impact of introducing demes (subpopulations) and a limited migration strategy in this version of QILGP. It outperforms AIMGP by obtaining better solutions with fewer parameters and operators. The performance improvement achieved by this latest version of QILGP encourages its ongoing and future enhancements. Thus, this paper concludes that the quantum inspiration paradigm can be a competitive approach to evolve programs more efficiently.

关键词： quantum-inspired algorithm multilevel quantum system linear genetic programming

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A Multi-Objective Decomposition Optimization Method for Refinery Crude Oil Scheduling through Genetic Programming

A Multi-Objective Decomposition Optimization Method for Refi...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Pereira, Cristiane Salgado Dias, Douglas Mota Marti, Luis Vellasco, Marley Petrobras Res & Dev Ctr Rio De Janeiro Brazil Univ Limerick Limerick Ireland Inria Chile Res Ctr Santiago Chile Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9798400701207

This paper proposes an evolutionary algorithm integrating genetic programming and a decomposition-based multi-objective algorithm to address a crude oil refinery scheduling problem. Four objectives are modelled, two related to maintaining the crude oil processing level, and the other two aim to keep the refinery operations as smooth as possible. The proposed method, Constrained-Decomposition of quantum-inspired Grammar-based Linear Genetic Programming (C-DQIGLGP), uses quantum-inspired Grammar-based Linear Genetic Programming (QIGLGP), replacing its hierarchical approach for the objectives with a multi-objective decomposition-based one. To achieve this goal, QIGLGP was profoundly modified regarding sorting individuals, updating the population, and applying the evolutionary operator. Individuals whose objective values related to processing level are under a predefined limit are better ranked. We compare the results of C-DQIGLGP for five scenarios from a real refinery to those obtained by QIGLGP and Constrained Non-dominated Sort QIGLGP (C-NSQIGLGP), from literature, demonstrating the better performance of C-DQIGLGP for all cases.

关键词： evolutionary multi-objective optimization decomposition quantum-inspired algorithm genetic programming refinery scheduling

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quantum-inspired Multi-Gene Linear Genetic Programming Model for Regression Problems 3

Quantum-Inspired Multi-Gene Linear Genetic Programming Model...

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3rd Brazilian Conference on Intelligent Systems (BRACIS)

作者： Strachan, Guilherme C. Koshiyama, Adriano S. Dias, Douglas M. Vellasco, Marley M. B. R. Pacheco, Marco A. C. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil

ISBN: (纸本)9781479978595

We propose the quantum-inspired Multi-Gene Linear Genetic Programming (QIMuLGP), which is a generalization of quantum-inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming;Multi-Gene tree-based GP (MGGP);and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its runtime always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.

关键词： quantum-inspired algorithm multi-gene genetic programming symbolic regression

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quantum Artificial Bee Colony algorithm for Knapsack Problem

Quantum Artificial Bee Colony Algorithm for Knapsack Problem

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Liu, Zhen Hu, Yunan Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Shandong Peoples R China

ISBN: (纸本)9783037855447

The traditional quantum evolutionary algorithm takes a long time to converge and can be easy trap into local optima. In order to overcome and accelerate the speed of the convergence, a new quantum evolutionary algorithm is proposed in the paper. The proposed new algorithm named discrete quantum bee colony algorithm incorporate the basic idea of the artificial bee colony algorithm. The initial population can be initialized randomly using quantum encoded and the population can be formed by there parts and every subpopulation can evolve cooperatively. In the end, the individual will rated according to the multi-granularity mechanism and also rated according to the evolution condition. Simulation results of knapsack problems show that the proposed algorithm performs better than other algorithm.

关键词： quantum-inspired algorithm Knapsack problem Chaotic

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Large set microstructure reconstruction mimicking quantum computing approach via deep learning

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ACTA MATERIALIA 2022年第0期230卷

作者： Liu, Yanming Chen, Shu Jian Sagoe-Crentsil, Kwesi Duan, Wenhui Monash Univ Dept Civil Engn Clayton 3168 Australia Univ Queensland Sch Civil Engn St Lucia 4072 Australia

Microstructural characterization and reconstruction (MCR) is critical for unearthing processing-structure property (PSP) links in new materials discovery, design and development. However, the inherent generation of large sets of digital microstructures remains challenging due to the optimization requirements of conventional MCR platforms. In this study, we designed a new framework of MCR mimicking quantum computing (QC) approach to boost the speed of reconstructions. A 2D probabilistic map was utilized as input which contains multi point correlation functions of the microstructure. The designed framework generates 3D microstructures from the probabilistic map based on a set of parameters calibrated via a deep learning algorithm. Such a framework converts the optimization process of MCR into a parameter extraction process replicating Shor's algorithm. The improved efficiency allows material scientists to build sensible PSP links via simulation and data-mining techniques. This method also demonstrates a potential methodology to achieve quantum supremacy with the aid of deep learning using a classical computer. (c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

关键词： Processing-structure-property links Microstructure characterization and reconstruction Computational material quantum-inspired algorithm Deep learning

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

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115615-115615页

作者： Xin, Gang Wang, Peng Jiao, Yuwei Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Peoples R China Southwest Univ Nationalities Sch Comp Sci & Technol Chengdu 610225 Peoples R China

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 uniand multimodal benchmark functions. The experimental results illustrate that the proposed method has a comparable performance for numerical optimization problems.

关键词： quantum-inspired algorithm Time-dependent Schrodinger equation Numerical optimization Multiple quantum perturbations

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