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نتیجه جستجو - بهینه سازی ترکیبی

تعداد مقالات یافته شده: 14
ردیف عنوان نوع
1 Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمان‌بندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینه‌سازی ترکیبی توسط محاسبات کوانتومی-2022
Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in 6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem, we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing. This will lead to the optimization of the entire system at a high speed and low power consumption in the future. Using computer simulation, we clarified that the proposed method realizes a more efficient communication compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization
مقاله انگلیسی
2 Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
لایه VQE: یک رویکرد متغیر برای بهینه سازی ترکیبی در کامپیوترهای کوانتومی پر سر و صدا-2022
Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to 40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results show that L-VQE performs well under realistic hardware noise.
INDEX TERMS: Combinatorial optimization | hybrid quantum-classical algorithm | quantum optimization.
مقاله انگلیسی
3 Quantum Computing Based Optimization for Intelligent Reflecting Surface (IRS)-Aided Cell-Free Network
بهینه‌سازی مبتنی بر محاسبات کوانتومی برای شبکه‌های بدون سلول با کمک سطح بازتابی هوشمند (IRS)-2022
Intelligent reflecting surface (IRS) enables the control of propagation characteristics and is attracting considerable attention as a technology to improve energy utilization efficiency in 6th generation mobile communication systems. As cell-free networks with multiple distributed base stations (BSs) can communicate in a coordinated manner, they are being actively researched as a new network architecture to resolve the problem of inter-cell interference in conventional cellular networks. The introduction of the IRS into the cell-free network can avoid shadowing at a lower cost with less power consumption. Thus, in this study, we considered the case of communication with user equipment (UE) in a shadowing environment using IRS in a cell-free network that contained distributed BSs with a single antenna. Moreover, the selection of multiple access methods was derived according to the numbers of BSs, IRSs, and UEs. In addition, we proposed a quadratic unconstrained binary optimization formulation to optimize the IRS reflection coefficient using quantum computing. The simulation results verified that the application of the proposed method resulted in a more efficient communication. Thus, this study clarifies that the optimum control method in every communication environment and aims to act as a stepping stone to optimize the entire cell-free system.
Index Terms: Intelligent Reflecting Surface | Cell-Free Network | Quantum Computing | Quantum Annealing | Combinatorial Optimization.
مقاله انگلیسی
4 Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022
Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized by p, initialization of variational parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms.
مقاله انگلیسی
5 Tabu search for min-max edge crossing in graphs
جستجوی تابو برای عبور از لبه های حداقل حداکثر در گراف ها -2020
Graph drawing is a key issue in the field of data analysis, given the ever-growing amount of information available today that require the use of automatic tools to represent it. Graph Drawing Problems (GDP) are hard combinatorial problems whose applications have been widely relevant in fields such as social network analysis and project management. While classically in GDPs the main aesthetic concern is re- lated to the minimization of the total sum of crossing in the graph (min-sum), in this paper we focus on a particular variant of the problem, the Min-Max GDP, consisting in the minimization of the maximum crossing among all egdes. Recently proposed in scientific literature, the Min-Max GDP is a challenging variant of the original min-sum GDP arising in the optimization of VLSI circuits and the design of in- teractive graph drawing tools. We propose a heuristic algorithm based on the tabu search methodology to obtain high-quality solutions. Extensive experimentation on an established benchmark set with both previous heuristics and optimal solutions shows that our method is able to obtain excellent solutions in short computation time.
Keywords: Combinatorial optimization | Graph drawing | Metaheuristics
مقاله انگلیسی
6 A new approach for identifying the Kemeny median ranking
یک روش جدید برای شناسایی رتبه بندی متوسط Kemeny-2020
Condorcet consistent rules were originally developed for preference aggregation in the theory of social choice. Nowadays these rules are applied in a variety of fields such as discrete multi-criteria analysis, defence and security decision support, composite indicators, machine learning, artificial intelligence, queries in databases or internet multiple search engines and theoretical computer science. The cycle issue, known also as Condorcets paradox, is the most serious problem inherent in this type of rules. Solutions for dealing with the cycle issue properly already exist in the literature; the most important one being the identification of the median ranking, often called the Kemeny ranking. Unfortunately its identification is a NP-hard problem. This article has three main objectives: (1) to clarify that the Kemeny median order has to be framed in the context of Condorcet consistent rules; this is important since in the current practice sometimes even the Borda count is used as a proxy for the Kemeny ranking. (2) To present a new exact algorithm, this identifies the Kemeny median ranking by providing a searching time guarantee. (3) To present a new heuristic algorithm identifying the Kemeny median ranking with an optimal trade-off between convergence and approximation .
Keywords : Decision analysis | Combinatorial optimisation | Social choice| Multiple criteria | Artificial intelligence| Defence and security| Big data
مقاله انگلیسی
7 Globally-biased BIRECT algorithm with local accelerators for expensive global optimization
الگوریتم BIRECT مغرضانه جهانی با شتاب دهنده های محلی برای بهینه سازی جهانی ارزشمند-2019
In this paper, black-box global optimization problem with expensive function evaluations is considered. This problem is challenging for numerical methods due to the practical limits on computational budget often required by intelligent systems. For its efficient solution, a new DIRECT-type hybrid technique is proposed. The new algorithm incorporates a novel sampling on diagonals and bisection strategy (instead of a trisection which is commonly used in the existing DIRECT-type algorithms), embedded into the globally-biased framework, and enriched with three different local minimization strategies. The numerical results on a test set of almost 900 problems from the literature and on a real-life application regarding nonlinear regression show that the new approach effectively addresses well-known DIRECT weaknesses, has beneficial effects on the overall performance, and on average, gives significantly better results compared to several DIRECT-type methods widely used in decision-making expert systems.
Keywords: Nonlinear global optimization| DIRECT-type algorithms | BIRECT algorithm | hybrid optimization algorithms | nonlinear regression
مقاله انگلیسی
8 الگوریتم بهینه سازی ترکیبی MIGA و NLPQL برای بهینه سازی پارامترهای bus الکتریکی هیبریدی پلاگین
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 11
در این مقاله، اقتصاد سوخت به عنوان هدف بهینه سازی bus الکتریکی هیبریدی پلاگین (PHEB) انتخاب شده است. مدل ریاضی بهینه سازی پارامترهای نیروی برق PHEB است که بر اساس استراتژی مدیریت انرژی مطلوب است و استراتژی مدیریت انرژی این الگوریتم با استفاده از الگوریتم برنامه نویسی پویا (DP) انجام میشود. در مرحله اول، اقتصاد سوخت PHEB به عنوان هدف تابع بهینه سازی پارامتر انتخاب شد. سپس، الگوریتم بهینه سازی ترکیبی توسط الگوریتم ژنتیک چند جزیره (MIGA) و برنامه نویسی مستطیلی NLPQL طراحی شد. در ابتدا MIGA برای بهینه سازی جهانی مورد استفاده قرار گرفت و NLPQL برای بهینه سازی محلی استفاده شد. در نهایت، نتایج آزمایشات نشان داد که مصرف سوخت PHEB در هر 100 کیلومتر از 18.51 لیتر دیزل به 17.41 لیتر دیزلی رسید و مصرف برق در هر 100 کیلومتر، در سطح یکسانی حفظ شد.
کلمات کلیدی: بهینه سازی پارامترها | bus الکتریکی هیبریدی | الگوریتم ژنتیک چند جزیره | برنامه نویسی درجه دوم مرتبه NLPQL
مقاله ترجمه شده
9 Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators
امنیت مبتنی بر الگوریتم PSO-APO ترکیبی چند هدفه محدود جریان نیروی مطلوب با ژنراتورهای باد و حرارت-2017
In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF) problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultane- ously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of get- ting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large inter- connected power system to solve MO-SCOPF problem with integration of wind and thermal generators.© 2017 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:Multi-objective | Hybrid optimization algorithm | Security constrained optimal power flow | Pareto optimal solution
مقاله انگلیسی
10 A framework for secure IT operations in an uncertain and changing environment
یک چارچوب برای عملیات IT ایمن در محیط نامطمئن و در حال تغییر-2017
Article history:Received 5 February 2016Revised 2 March 2017Accepted 17 April 2017Available online 18 April 2017Keywords:Information securityIT security management Decision support framework Security investment decisions Combinatorial optimizationIn this paper, a quantitative approach is proposed that addresses various decision making challenges within the IT security process of an organization. The approach serves as a framework that facilitates multiple applications to optimize the security of IT systems in different environmental settings. Address- ing this problem is a critical challenge for almost all organizations and it still lacks a comprehensive and consistent quantitative treatment. The key question of the corresponding decision problem is which safeguards to select in order to achieve sufficient security. The proposed framework addresses this by establishing a generally applicable problem structure and by reusing existing knowledge in order to re- duce implementation costs of the approach. Based on this foundation, efficient MILP models are applied to support the establishment of an effective IT security strategy. Depending on the knowledge an organi- zation is able to provide, decisions take uncertainty and even dynamic aspects into account. As a result, deployed safeguards are robust against uncertain security threats and remain stable over several plan- ning periods even if the system or the threat environment changes. This is a significant advancement that results in higher security in the short-term and lower costs in the mid- and long-term.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Information security | IT security management | Decision support framework | Security investment decisions | Combinatorial optimization
مقاله انگلیسی
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