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An Integrated Online-Offline Hybrid Particle Swarm Optimization Framework for Medium Scale Expensive Problems*

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An Integrated Online-Offline Hybrid Particle Swarm Optimizat...

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Hanyu Hu Jian Wang Xiaodong Huang Sergey V. Ablameyko College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of science) China University of Petroleum (East China) Qingdao China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Real-world optimization problems often involve computationally expensive evaluations, making efficient optimization crucial. To address medium-scale expensive optimization problems, this paper proposes and investigates an Integrated Online-Offline Hybrid Particle Swarm Optimization Framework (IOOPSOF). The framework integrates concepts from active learning and ensemble learning, iteratively querying the most uncertain and highest-precision solutions to achieve efficient utilization of evaluation resources. IOOPSOF employs two distinct sample selection criteria, with offline optimization for exploration and online optimization for exploitation. The effectiveness of the proposed method is validated through experiments on a series of low to medium-dimensional benchmark functions and the operational parameter optimization of Enhanced Geothermal Systems (EGS). Results demonstrate that IOOPSOF significantly outperforms existing methods, achieving global optimum (0) on most benchmark functions and showing superior performance over single surrogate-assisted evolutionary algorithms (SAEAs) on real-world problems, thereby showcasing its exceptional potential for solving expensive optimization problems.

关键词： Decision making Benchmark testing Computational efficiency Ensemble learning Particle swarm optimization Complex systems Optimization Standards

Semantic Data Representation for Explainable Windows Malware Detection Models

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

Metric Spaces in Which Many Triangles Are Degenerate

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arXiv 2024年

作者： Chvátal, Vašek de Rancourt, Noé Quintero, Guillermo Gamboa Kantor, Ida Szabó, Péter G.N. Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Applied Mathematics Charles University Prague Czech Republic Univ. Lille CNRS UMR 8524 Laboratoire Paul Painlevé LilleF-59000 France Computer Science Institute of Charles University Prague Czech Republic Department of Engineering Mathematics Faculty of Engineering and Information Technology University of Pécs Pécs Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary

Richmond and Richmond (American Mathematical Monthly 104 (1997), 713–719) proved the following theorem: If, in a metric space with at least five points, all triangles are degenerate, then the space is isometric to a subset of the real line. We prove that the hypothesis is unnecessarily strong: In a metric space on n points, fewer than 7n2/6 suitably placed degenerate triangles suffice. However, fewer than n(n − 1)/2 degenerate triangles, no matter how cleverly placed, never *** Codes 30L99, 51F99, 54E99 © 2024, CC BY.

关键词： Topology

Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

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arXiv 2024年

作者： Huang, PoTsang B. Rizwan, Muhammad Ali, Mehboob Department of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Department of Industrial and Systems Engineering Chung Yuan Christian University Zhongli Taiwan Department of Applied Mathematics Chung Yuan Christian University Zhongli Taiwan

The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness. © 2024, CC BY.

关键词： Diseases

Evaluating the performance of the Quantum Approximate Optimisation Algorithm to solve the Quadratic Assignment Problem

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Evaluating the performance of the Quantum Approximate Optimi...

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： M. T. Khumalo K. Prag K. J. Nixon School of Computer Science and Applied Mathematics School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa

The performance of the Quantum Approximate Optimisation Algorithm (QAOA) in solving the Quadratic Assignment Problem (QAP) is evaluated, with the Variational Quantum Eigensolver (VQE) as a benchmark. The QAP is directly revelant to numerous industry scenarios. The QAP, a Combinatorial Optimisation Problem (COP), is classified as $\mathcal{NP}$ -Hard. This classification means CPU time increases exponentially as the problem size scales when solving the QAP using deterministic optimisation techniques. Therefore, this work investigates the QAOA in search of a non-deterministic optimisation technique to efficiently obtain solutions to the QAP. This research compares two warm start techniques to solve QAP instances of sizes 3 to 7. The metrics of comparison - that measure efficiency and solution quality - were introduced in previous work on this topic. For the QAOA, the impact of the p-value, a determination of circuit depth, is investigated. Of the two quantum hybrid heuristics, the VQE retrieves solutions in a shorter computational time with a smaller circuit size, which allows for solving instances with a larger problem size. Compared to the VQE, the QAOA performs better in terms of feasibility as the problem size scales. The quantum warm start method results implies that the QAOA may not maintain higher solution quality for instances larger than size 4. Still, further investigation should be conducted once quantum devices with more qubits and higher quantum volumes are available.

关键词： Performance evaluation Industries Uncertainty Qubit Benchmark testing Approximation algorithms Classification algorithms

Forecasting day-ahead electric power prices with functional data analysis

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Frontiers in Energy Research 2025年 13卷

作者： Jan, Faheem Iftikhar, Hasnain Tahir, Mehwish Khan, Mehak Department of Mathematics and Statistics Bacha Khan University Charsadda Pakistan Department of Statistics Quaid-i-Azam University Islamabad Pakistan Department of Mathematics Shaheed Benazir Bhutto Women University Peshawar Pakistan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Day-ahead electricity prices in today’s competitive electric power markets have complex features such as high frequency, high volatility, non-linearity, non-stationarity, mean reversion, multiple periodicities, and calendar effects. These complicated features make price forecasting difficult. To address this, this research examines the application of functional data analysis to forecasting day-ahead electric power prices. Compared to classical time series forecasting approaches, functional data analysis is more appealing since it anticipates the daily profile, allowing for short-term projections. This technique uses a functional autoregressive ((Formula presented.) AR) and a functional autoregressive with exogenous predictors ((Formula presented.) AR (Formula presented.)) model to predict the next-day electric power prices. In addition, standard time-series forecasting models, including autoregressive (AR) AR (Formula presented.), autoregressive integrated moving average (ARIMA), and ARIMA (Formula presented.) are also utilized for comparison. The model’s prediction performance was evaluated using data on electricity prices from the British electricity market, considering forecast error indicators and the same forecast statistical test. The results show that the proposed functional models ((Formula presented.) AR and (Formula presented.) AR (Formula presented.)) outperform standard time series models. In comparison to the benchmark models (AR, AR (Formula presented.), ARIMA, ARIMA (Formula presented.), and the proposed (Formula presented.) AR model), the (Formula presented.) AR (Formula presented.) model reduces: the day-ahead forecasting average MAPE by ranges of 5.02%–45.77%, 4.07%–40.63%, 3.80%–38.99%, 1.90%–24.22%, and 0.95%–13.78%;MAE by ranges of 9.43%–69.32%, 5.17%–65.48%, 6.04%–59.16%, 3.02%–42.01%, and 1.51%–26.59%;RMSE by ranges of 8.98%–40.97%, 6.68%–34.03%, 4.22%–24.58%, 3.91%–23.20%, and 2.30%–15.11%. Furthermore, compared with the literature-proposed b

关键词： classical time series models day-ahead electricity price forecasting electric power market functional data analysis functional time series models

Using Ontologies for the Development of the University Educational Ecosystem 22

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Using Ontologies for the Development of the University Educa...

22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

作者： Pustovalova, Natalya Avdeenko, Tatiana Pustovalova, Alexandra Novosibirsk State Technical University Dept. Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Dept. Applied Mathematics and Computer Science Novosibirsk Russia

ISBN: (纸本)9781665414982

The paper considers the possibility of improving the educational ecosystem by using ontologies in the architecture of the university electronic information environment. Various types of actors of the modern educational ecosystem, interacting within the educational process with educational resources, are presented;their needs that are relevant for its development shortly are analyzed. This paper describes a general approach to using ontology as an element of the IT architecture of a university. Formalizing objects of the educational process in the form of ontology (OWL, RDF models) allows creating various applications for its intellectual support, ensuring the uniformity of architectural solutions and further evolutionary development of the university information environment based on the IEEE LOM and TinCanAPI standards. © 2021 IEEE.

关键词： Ontology

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs

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arXiv 2024年

作者： Awadelkarim, Elsiddig Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics City University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important class of processes, which frequently appear in applications such as mathematical finance, biology and opinion dynamics. Typically the stationary distribution is unknown and indeed one cannot simulate such processes exactly. As a result one commonly requires a time-discretization scheme which results in a discretization bias and a bias from not being able to simulate the associated stationary distribution. To overcome this bias, we present a new unbiased estimator taking motivation from the literature on unbiased Monte Carlo. We prove the unbiasedness of our estimator, under assumptions. In order to prove this we require developing ergodicity results of various discrete time processes, through an appropriate discretization scheme, towards the invariant measure. Numerous numerical experiments are provided, on a range of MVSDEs, to demonstrate the effectiveness of our unbiased estimator. Such examples include the Currie-Weiss model, a 3D neuroscience model and a parameter estimation problem. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

Probabilistic machine learning for enhanced chiller sequencing: A risk-based control strategy

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Energy and Built Environment

Energy and Built Environment 2024年

作者： Chen, Zhe Zhang, Jing Xiao, Fu Madsen, Henrik Xu, Kan Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong Research Institute for Smart Energy The Hong Kong Polytechnic University Hong Kong Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

Multiple-chiller systems are widely adopted in large buildings due to their high flexibility and efficiency in providing cooling capacity. A reliable and robust chiller sequencing control strategy is crucial to ensure the energy efficiency and stability of the multiple-chiller systems. However, conventional chiller sequencing control strategies are usually based on real-time measured cooling load without considering the cooling load changes in the following hours. Conventional rule-based strategy may result in unnecessary switching on and off, leading to energy waste and impairing system stability. Therefore, this study proposes a robust chiller sequencing control strategy that utilizes probabilistic cooling load predictions. 1h-ahead probabilistic cooling load prediction in the form of the normal distribution is made using natural gradient boosting (NGBoost). Compared to conventional machine learning algorithms, NGBoost can predict not only the future cooling load but also the uncertainty of the predicted cooling load, which enables the load prediction to handle the uncertainties associated with the data/measurements adequately. A novel risk-based sequencing strategy is developed based on the probabilistic cooling load predictions. The data experiment shows that the proposed strategy can significantly improve the stability and reliability of the chiller plant by reducing the total switching number by up to 43.6 %. © 2024

关键词： Normal distribution