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Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A scalable optimization Framework

IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第2期70卷 1423-1441页

作者： Xu, Dongfang Jamali, Vahid Yu, Xianghao Ng, Derrick Wing Kwan Schober, Robert Friedrich Alexander Univ Erlangen Nurnberg FAU Inst Digital Commun D-91058 Erlangen Germany Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-of-service requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physics-based model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promis

关键词： optimization Wireless communication Resource management Receivers Array signal processing Computational complexity Radio frequency Branch and bound approach optimal resource allocation intelligent reflecting surface scalable optimization

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Population Stream-Driven scalable Evolutionary Many-Objective optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1406-1417页

作者： Chen, Huangke Wu, Guohua Wang, Rui Pedrycz, Witold Cent South Univ Sch Automat Changsha 410083 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

Solving multi-objective optimization problems with scalable decision variables and objectives is an ongoing challenging task. This study proposes a new evolutionary framework that a series of continuously generated subpopulations are used to approximate the entire Pareto-optimal front. These dynamic subpopulations are abstracted as a population stream. In this framework, one subpopulation is only responsible for searching for a Pareto-optimal solution. Diversity is emphasized among converged solutions coming from different subpopulations, striving to alleviate the conflict between diversity and convergence. To improve the convergence of the newly generated subpopulations, the polynomial fitting method is performed on the obtained solutions to model the relationships among decision variables, which are then used to assist in the generation of new subpopulations. Moreover, an adaptive granularity grid-based environmental selection strategy is proposed to maintain a set of well-diversifying converged solutions. Lastly, extensive experiments are conducted to demonstrate the proposal's superiority by comparing it with 19 representative algorithms in 45 test instances with 3-15 objectives and 300-1500 decision variables.

关键词： Evolutionary computation Training large-scale many-objective population stream scalable optimization Evolutionary computation Training large-scale many-objective population stream scalable optimization

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Tactical capacity planning for semiconductor manufacturing: MILP models and scalable distributed parallel algorithms

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AICHE JOURNAL 2016年第11期62卷 3930-3946页

作者： Zhou, Rui-Jie Li, Li-Juan Stanford Univ Dept Elect Engn Stanford CA 94305 USA Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12180 USA

A multiperiod stochastic mixed-integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in-process inventory, demand and capacity variability, multisite production, capacity utilization rate, and downside risk management. Both planning level decisions (i.e., capacity allocation and customer service level decisions) as well as operational level decisions (i.e., production, inventory, and shipment decisions) can be simultaneously determined based on the two proposed multiobjective optimization models. To address the huge number of scenarios needed to characterize the uncertainty and the large number of first-stage integer variables in industrial scale applications, two novel scalable distributed parallel optimization algorithms are developed to mitigate the computational burden. The proposed mathematical models and algorithms are illustrated through two case studies from a major US semiconductor manufacturer. Results from these case studies provide key decision support for capacity expansion in semiconductor industry. (c) 2016 American Institute of Chemical Engineers AIChE J, 62: 3930-3946, 2016

关键词： semiconductor manufacturing capacity planning two-stage stochastic integer optimization distributed parallel optimization scalable optimization

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A simple and robust Monte Carlo hybrid local search algorithm for the facility location problem

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ENGINEERING optimization 2019年第5期51卷 832-845页

作者： Chalupa, D. Nielsen, P. Aalborg Univ Dept Mat & Prod Operat Res Grp Aalborg Denmark

In this article, a new Monte Carlo hybrid local search algorithm (Hyb-LS) is proposed for solving the uncapacitated facility location problem. Hyb-LS is based on repeated sampling using two local search strategies based on best improvement and randomized neighbourhood search. A major advantage of Hyb-LS for its practical use is that the number of restarts is its only parameter to tune. The algorithm is also simple to reimplement, scalable and robust to changes in coefficients within a problem instance. The stopping criterion for local search is learned automatically. Experimental results are presented for four representative and contrasting cost and distance models. The results obtained by Hyb-LS are compared to the optimal or near-optimal solutions found by a mixed integer linear programming (MILP) solver with a generous time limit. For three out of the four models, Hyb-LS obtains better solutions than the upper bound found by the MILP solver for at least one instance.

关键词： Facility location problem hybrid local search Monte Carlo methods metaheuristics scalable optimization

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Neural Network-Based Game Theory for scalable Offloading in Vehicular Edge Computing: A Transfer Learning Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 7431-7444页

作者： Zhang, Juan Wu, Yulei Min, Geyong Li, Keqin Univ Exeter Dept Comp Sci Exeter EX4 4QF England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England Univ Bristol Fac Engn Bristol BS8 1UB England Univ Bristol Bristol Digital Futures Inst Bristol BS8 1UB England SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

With the unprecedented scalability issues rising in vehicular edge computing (VEC), we argue in this paper that the scalability, along with the remarkable growth of demands for offloading, should be integrated into the modelling for effective offloading decision-making strategies requested by a large number of vehicles. A two-stage game-theory model can depict offloading decision-making strategies by considering both the revenue of network operators and the cost of VEC users. However, heuristic processes of solving such models show significant limitations in terms of high computational complexity and energy consumption due to the changing VEC environment. Therefore, our objective in this study is to solve the game-theory model efficiently and achieve scalable offloading for the changing VEC environment. We first develop a two-stage game-theory model for the offloading decision-making strategy for VEC, by which an operator's revenue, energy consumption and latency are considered. Then a neural network (NN) model is designed to learn the predicted behaviours of the established game-theory model for offloading decisions in a more efficient manner. After that, a feature-based transfer learning algorithm is proposed for scalable offloading optimization under unseen VEC environments. Experimental results show that the proposed NN can significantly improve the efficiency of solving the game theory model, and the developed transfer learning approach can effectively achieve the scalability of offloading decisions in a changing VEC environment. The results demonstrate that the accuracy of the proposed transfer learning approach is 37% higher than that of several state-of-the-art algorithms, and the runtime halves.

关键词： Game theory mobile edge computing neural networks offloading scalable optimization

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Optimal scheduling of casting sequence using genetic algorithms

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MATERIALS AND MANUFACTURING PROCESSES 2003年第3期18卷 409-432页

作者： Deb, K Reddy, AR Singh, G Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab Kanpur 208016 Uttar Pradesh India

Scheduling a casting sequence involving a number of orders with different casting weights and satisfying due dates of is an important optimization problem often encountered in foundries. In this article, we attempt to solve this complex, multi-variable, and multi-constraint optimization problem by using different implementations of genetic algorithms (GAs). In comparison with a mixed-integer linear programming solver, GAs with problem-specific operators are found to provide faster (with a subquadratic computational time complexity) and more reliable solutions to very large (more than 1 million integer variables) casting sequence optimization problems. In addition to solving the particular problem, the study demonstrates how problem-specific information can be introduced in a GA for solving complex real-world problems.

关键词： scheduling genetic algorithms large-scale optimization mixed-integer linear programming knowledge-based GA scalable optimization subquadratic complexity

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scalable distributed optimal control of vibrating modular structures

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STRUCTURAL CONTROL & HEALTH MONITORING 2020年第4期27卷 e2502-e2502页

作者： Pisarski, Dominik Konowrocki, Robert Jankowski, Lukasz Polish Acad Sci Inst Fundamental Technol Res Dept Intelligent Technol Warsaw Poland

A scalable optimal control method for structural vibration mitigation is studied. The method relies on a structure's partitioning that leads to a set of dynamically interconnected subsystems. Each subsystem is operated with an individual subcontroller that collects the local state information and collaborates with the neighboring subcontrollers to estimate a short time prediction of the interconnecting forces defining the subsystem's boundary conditions. Using the extended model that represents the subsystem's dynamics together with the evolution of its boundary conditions, each subcontroller computes the control decision based on the solution to a finite-time horizon optimal control problem. In order to cope with the changes in the boundary conditions, the optimal solution is computed repetitively according to the receding horizon scheme. The method is validated numerically for a cantilever structure equipped with actively controlled electromagnetic actuators and subjected to a variety of initial condition scenarios. The performance of the designed controller is tested by comparisons to the centralized and isolated decentralized controllers. The introduced system partitioning and distributed controller allow performing parallel computing which makes the method fully scalable and applicable to large-scale structures. The computational complexity of the designed distributed control is studied for different settings in the modeling of the subsystem's boundary conditions.

关键词： active control distributed control modular structure scalable optimization stabilization

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A scalable approach for continuous time Markov models with covariates

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BIOSTATISTICS 2023年第3期25卷 681-701页

作者： Hatami, Farhad Ocampo, Alex Graham, Gordon Nichols, Thomas E. Ganjgahi, Habib Univ Oxford Big Data Inst Li Ka Shing Ctr Hlth Informat & Discovery Dept Med Oxford OX3 7LF England Univ Oxford Dept Stat Oxford OX3 7LF England Novartis Pharm AG CH-4056 Basel Switzerland Univ Oxford Big Data Inst Li Ka Shing Ctr Hlth Informat & Discovery Nuffield Dept Populat Hlth Oxford OX3 7LF England Univ Oxford Wellcome Ctr Integrat Neuroimaging Nuffield Dept Clin Neurosci FMRIB Oxford OX3 9DU England Univ Oxford Dept Stat 24-29 St Giles Oxford OX1 3LB England

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Pade approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Pade expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis *** data set.

关键词： Continuous-time Markov model Multiple sclerosis Multistate model Pade approximation scalable optimization

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Traffic optimization by Local Bacterial Memetic Algorithm 15th

Traffic Optimization by Local Bacterial Memetic Algorithm

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15th International Conference on Computational Collective Intelligence (ICCCI)

作者： Kovacs, Szilard Barta, Zoltan Botzheim, Janos Eotvos Lorand Univ Pazmany P Setany 1-A H-1117 Budapest Hungary

ISBN: (纸本)9783031414558;9783031414565

Transport is an essential part of our lives. Optimizing transport provides significant economic and life quality improvements. Real-time traffic optimization is possible with the help of a fast communication network and decentralized sensing in smart cities. There are several analytical and simulation-based methods for traffic optimization. Analytical solutions usually look at more straightforward cases, while simulations can also consider the behavior of individual drivers. This article focuses on optimization methods and provides efficient traffic control based on simulations. The optimization goal is to find the proper sequence and timings of traffic light signals to ensure maximum throughput. In the article only the waiting time is selected as optimization criterion, but with knowledge of the vehicle stock (fuel type, fuel consumption, startstop settings, number of passengers, etc.) it can be easily expanded to multi-objective optimization. In the literature, there are many optimization solutions, but all have some disadvantages mainly the scalability and the connectivity. Bacterial evolutionary algorithm and hill climbing algorithm are proposed in this paper with special area operators for the traffic optimization task. The developed memetic optimization algorithm can be efficiently scaled to optimize the traffic of even large cities. The method is efficient and well parallelized for real-time optimization use. For this study, a part of the city is examined in a SUMO simulation environment. The simulation result shows that our scalable memetic algorithm outperforms the currently applied methods by 35-45%.

关键词： traffic control memetic algorithm scalable optimization smart city evolutionary computing

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Probabilistic Model-Building Genetic Algorithms 12

Probabilistic Model-Building Genetic Algorithms

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Pelikan, Martin Univ Missouri Dept Math & Comp Sci MEDAL St Louis MO 63121 USA

ISBN: (纸本)9781450311786

Probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Building and sampling probabilistic models of promising solutions enables the use of machine learning techniques for automated discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. The tutorial Probabilistic Model-Building GAs provides an introduction to PMBGAs with an overview of major research directions in this area.

关键词： scalable optimization probabilistic model-building genetic algorithms iterated density-estimation algorithms metaheuristics optimization graphical models machine learning evolutionary computation estimation of distribution algorithms

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