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1 |
Fuzzy Logic on Quantum Annealers
منطق فازی در آنیل های کوانتومی-2022 Quantum computation is going to revolutionize the world of
computing by enabling the design of massive parallel algorithms that solve
hard problems in an efficient way, thanks to the exploitation of quantum
mechanics effects, such as superposition, entanglement, and interference.
These computational improvements could strongly influence the way how
fuzzy systems are designed and used in contexts, such as Big Data, where
computational efficiency represents a nonnegligible constraint to be taken
into account. In order to pave the way toward this innovative scenario,
this article introduces a novel representation of fuzzy sets and operators
based on quadratic unconstrained binary optimization problems, so as to
enable the implementation of fuzzy inference engines on a type of quantum
computers known as quantum annealers.
Index Terms: Fuzzy logic | quantum computing | simulated annealing. |
مقاله انگلیسی |
2 |
New Advanced Computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer
معماری محاسباتی پیشرفته جدید برای طراحی و تحلیل رمزنگاری توسط D-Wave Quantum Annealer-2022 Universal quantum computers are far from achieving practical applications. The D-Wave quantum computer
is initially designed for combinatorial optimizations. Therefore, exploring the potential applications of the D-Wave
device in the field of cryptography is of great importance. First, although we optimize the general quantum Hamiltonian
on the basis of the structure of the multiplication table (factor up to 1 005 973), this study attempts to explore the
simplification of Hamiltonian derived from the binary structure of the integers to be factored. A simple factorization
on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the
D-Wave device. Second, by using the quantum computing cryptography based on the D-Wave 2000Q system, this
research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired
Simulated Annealing (QISA) framework. Good functions and a high-performance platform are introduced, and
additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found. Further
comparison between QISA and Quantum Annealing (QA) on six-variable bent functions not only shows the potential
speedup of QA, but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a
practical cryptography design.
Keywords: Quantum Annealing (QA) | factorization | Boolean functions | brain-inspired cognition |
مقاله انگلیسی |
3 |
A set of efficient heuristics and meta-heuristics to solve a multi-objective pharmaceutical supply chain network
مجموعه ای از روشهای اکتشافی و کارآیی کارآمد برای حل یک شبکه زنجیره تامین دارویی چند هدفه-2021 In this paper, we propose a new multi-objective optimization approach for the pharmaceutical supply chain network (PSCN) design problem to minimize the total cost and the delivery time of pharmaceutical products to the hospital and pharmacy, while maximizing the reliability of the transportation system. A new mixed-integer non-linear programming model was developed for the production-allocation-distribution-inventory-ordering- routing problem. Three new heuristics (H-1), (H-2), and (H-3) have been proposed and to validate the model, two new meta-heuristic algorithms, namely, an Improved Social Engineering Optimization (ISEO) and Hybrid Firefly and Simulated Annealing Algorithm (HFFA-SA) have been developed. The proposed mathematical model has been evaluated through extensive simulation experiments by analyzing different criteria. The results show that the proposed model along with the solution method provides a reliable and powerful instrument to solve the PSCN design problem. Keywords: Pharmaceutical supply chain network | Heuristic algorithms | Improved social engineering optimization | Hybrid firefly and simulated annealing | algorithm | Multi-objective optimization |
مقاله انگلیسی |
4 |
Truck scheduling in a multi-door cross-docking center with partial unloading : Reinforcement learning-based simulated annealing approaches
زمانبندی کامیون در یک مرکز متصل متقابل چند درب با تخلیه جزئی: رویکردهای بازپخت شبیه سازی شده مبتنی بر یادگیری تقویتی -2020 In this paper, a truck scheduling problem at a cross-docking center is investigated where inbound trucks are also
used as outbound. Moreover, inbound trucks do not need to unload and reload the demand of allocated destination,
i.e. they can be partially unloaded. The problem is modeled as a mixed integer program to find the
optimal dock-door and destination assignments as well as the scheduling of trucks to minimize makespan. Due to
model complexity, a hybrid heuristic-simulated annealing is developed. A number of generic and tailor-made
neighborhood search structures are also developed to efficiently search solution space. Moreover, some reinforcement
learning methods are applied to intellectually learn more suitable neighborhood search structures in
different situations. Finally, the numerical study shows that partial unloading of compound trucks has a crucial
impact on makespan reduction. Keywords: Logistics | Cross docking | Truck scheduling | Simulated annealing | Reinforcement learning |
مقاله انگلیسی |
5 |
Proposed Quantum AI solution for the Travelling Tournament Problem
راه حل پیشنهادی هوش مصنوعی کوانتومی برای مسئله مسابقات سفر-2020 In this paper, we propose an idea/solution for the
Travelling Tournament Problem, which is a unique
combinatorial problem tackling both feasibility and optimality
of the solution. Being an NP-Hard problem, generating
solutions can be extremely cost-ly, with most of the solutions of
the said problem having a time complexity of as high as O(n!).
Quantum Computing and artificial intelligence, on the other
hand, is making great strides, and we believe that the immense
computational potential of quantum computing can be used to
solve these scheduling problems. Artificial Intelligence and
machine learning present us with excellent frame-work
strategies that we have envisaged to incorporate in our
implementation. In the following paper, we delineate the
features of our solution and how we propose to incorporate the
quantum and classical aspects of computing. Keywords: Quantum Computing | NP-Hard Problem | Travelling Tournament Problem | Simulated annealing |
مقاله انگلیسی |
6 |
Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing
بهینه سازی پارامترهای یادگیری عمیق در پیش بینی مسافران اتوبوس مبتنی بر بازپخت شبیه سازی شده-2020 Bus is certainly one of the most widely used public transportation systems in a modern city because it
provides an inexpensive solution to public transportation users, such as commuters and tourists. Most
people would like to avoid taking a crowded bus on the way. That is why forecasting the number
of bus passengers has been a critical problem for years. The proposed method is inspired by the fact
that there is no easy way to know the suitable parameters for most of the deep learning methods
in solving the optimization problem of forecasting the number of passengers on a bus. To address
this issue, the proposed algorithm uses a simulated annealing (SA) to find out a suitable number of
neurons for each layer of a fully connected deep neural network (DNN) to enhance the accuracy rate in
solving this particular optimization problem. The proposed method is compared with support vector
machine, random forest, eXtreme gradient boosting, deep neural network, and deep neural network
with dropout for the data provided by the Taichung city smart transportation big data research center,
Taiwan (TSTBDRC). Our simulation results indicate that the proposed method outperforms all the other
forecasting methods for forecasting the number of bus passengers in terms of the accuracy rate and
the prediction time. Keywords: Bus transportation system | Simulated annealing | Deep learning | Hyperparameter optimization |
مقاله انگلیسی |
7 |
Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing
بهینه سازی پارامترهای یادگیری عمیق در پیش بینی مسافران اتوبوس مبتنی بر بازپخت شبیه سازی شده-2020 Bus is certainly one of the most widely used public transportation systems in a modern city because it
provides an inexpensive solution to public transportation users, such as commuters and tourists. Most
people would like to avoid taking a crowded bus on the way. That is why forecasting the number
of bus passengers has been a critical problem for years. The proposed method is inspired by the fact
that there is no easy way to know the suitable parameters for most of the deep learning methods
in solving the optimization problem of forecasting the number of passengers on a bus. To address
this issue, the proposed algorithm uses a simulated annealing (SA) to find out a suitable number of
neurons for each layer of a fully connected deep neural network (DNN) to enhance the accuracy rate in
solving this particular optimization problem. The proposed method is compared with support vector
machine, random forest, eXtreme gradient boosting, deep neural network, and deep neural network
with dropout for the data provided by the Taichung city smart transportation big data research center,
Taiwan (TSTBDRC). Our simulation results indicate that the proposed method outperforms all the other
forecasting methods for forecasting the number of bus passengers in terms of the accuracy rate and
the prediction time. Keywords: Bus transportation system | Simulated annealing | Deep learning | Hyperparameter optimization |
مقاله انگلیسی |
8 |
AI for Controlling the Flow and Pressure in Cooling Water Circuit of Power Plant
هوش مصنوعی برای کنترل جریان و فشار در مدار آب خنک کننده نیروگاه-2020 Best cooling system is that which require less
water and give optimum cooling to the system. Due to the
inefficiency of the present system to maintain the control the
flow of voltage using traditional techniques, to overcome the
existing problem requires a use of Artificial Intelligence in the
existing techniques. The concept of Simulated Annealing can
be used for solving the problem of voltage over loading and the
giving the optimal solution for the cooling water circuit of a
Thermal Power Plant. Keywords: Artificial Intelligence | cooling water circuit | Simulated Annealing | siphon |
مقاله انگلیسی |
9 |
Earth fissure hazard modeling using machine learning models
مدل سازی خطر شکستگی زمین با استفاده از مدل های یادگیری ماشین-2019 Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi3
arid basins. The excessive withdrawal of groundwater, as well as the other underground natural
4 resources, has been introduced as the significant causing of land subsidence and potentially, the
5 earth fissuring. Fissuring is rapidly turning into the nations’ major disasters which are
6 responsible for significant economic, social, and environmental damages with devastating
7 consequences. Modeling the earth fissure hazard is particularly important for identifying the
8 vulnerable groundwater areas for the informed water management, and effectively enforce the
9 groundwater recharge policies toward the sustainable conservation plans to preserve existing
10 groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the
11 hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary
12 involved to predict the earth fissures. This paper aims at proposing novel machine learning
13 models for prediction of earth fissuring hazards. The Simulated annealing feature selection
14 (SAFS) method was applied to identify key features, and the generalized linear model (GLM),
15 multivariate adaptive regression splines (MARS), classification and regression tree (CART),
16 random forest (RF), and support vector machine (SVM) have been used for the first time to build
17 the prediction models. Results indicated that all the models had good accuracy (>86%) and
18 precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear
19 model) had the lowest performance, while the RF model was the best model in the modeling
20 process. Sensitivity analysis indicated that the hazardous class in the study area was mainly
21 related to low elevations with characteristics of high groundwater withdrawal, drop in
22 groundwater level, high well density, high road density, low precipitation, and Quaternary
23 sediments distribution. Keywords: Hazard prediction | Geohazard | Earth fissure | 24 Machine learning |
مقاله انگلیسی |
10 |
تحلیل و پیشبینی کیفیت هوای شهری براساس الگوریتم هوشمند با بهینهسازی پارامتر و قوانین تصمیمگیری
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 18 آلودگی هوا به طور مداوم بر روی کره زمین تاثیر مخربی دارد، به اکوسیستمها آسیب میرساند، منابع طبیعی را تهی میکند و سلامت انسان را به خطر میاندازد. -=این مقاله الگوریتم هوشمند جدید را پیشنهاد میکند که شامل بهینهسازی پارامترها و قوانین تصمیمگیری برای پیشبینی و تحلیل کیفیت هوای شهری است. از طریق تحلیل دادههای بیست و چهار ساعته کیفیت هوای روزانه ارائه شده توسط ایستگاه نظارت بر کیفیت هوا در پکن، تبرید شبیهسازی شده (SA) و یک درخت تصمیم (DT) به عنوان عوامل اصلی پدیدار میشوند. ما ثابت میکنیم که در الگوریتم بررسی شده، میتوان از SA و DT برای تصمیمگیری قوانین و دستیابی دقیقتر برای طبقهبندی استفاده کرد. در مییابیم که میتوان از SA برای تنظیم بهترین تنظیمات پارامتر برای DT استفاده کرد. نتایج شبیهسازی نشان میدهد که دقت الگوریتم پیشنهادی برای طبقهبندی بسیار بهتر از سایر رویکردهای موجود است.
کلید واژه ها: کیفیت هوا | الگوریتم هوشمند جدید | تبرید شبیهسازی شده |
مقاله ترجمه شده |