With the increasing demand for cloud computing, the cloud storage systems are becoming more attractive to companies for their information processing, because of their scalability and low cost. Moreover, for companies ...
详细信息
ISBN:
(纸本)9781728164816
With the increasing demand for cloud computing, the cloud storage systems are becoming more attractive to companies for their information processing, because of their scalability and low cost. Moreover, for companies having multiple data centers in the different regions to handle and to access big data objects is one of the major problems, (as far as uploading and downloading to/from remote storages are concerned). One of the proposed solutions to this problem is distributed storage: i.e. to slice large objects into small chunks which are then uploaded to different cloud storages. As will be seen, this problem can be formalized as a constrained combinatorial optimization. In this paper, an optimal uploading strategy is developed to meet the various reliability criteria by solving the underlying combinatorial optimization.
Water allocation network (WAN) and heat exchange network (HEN) are effective optimization techniques in chemical process system engineering (CPSE). This paper reviews the literature about the optimization of water and...
详细信息
Water allocation network (WAN) and heat exchange network (HEN) are effective optimization techniques in chemical process system engineering (CPSE). This paper reviews the literature about the optimization of water and heat over the last 20 years. By analyzing the development of CPSE, this review presents a systematic overview on deterministic optimizationalgorithms and stochastic optimization algorithms. Deterministic opti-mization algorithms, like the conceptual method and mathematical programming method, could obtain a deterministic feasible solution by analyzing diagrams or solving the water-energy equations. stochastic opti-mization algorithms have a strong search ability in solution space. It doesn't need to solve complicated math-ematical models, and could avoid local optimum effectively and achieve the global optimal solution quickly. This paper summarizes the recent researches on stochastic optimization algorithms like simulated annealing algo-rithm (SA), genetic algorithm (GA), particle swarm algorithm (PSO), and so on. Finally, the application and future research gaps in this field are presented.
stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), estimation of dis-tribution algorithms (EDAs), and nested partitions algorithm (NPA) are used in many problems incl...
详细信息
stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), estimation of dis-tribution algorithms (EDAs), and nested partitions algorithm (NPA) are used in many problems including nonlinear model predictive control and task assignment. Some of these al-gorithms, however, lack global convergence guarantee such as PSO, or require strict convergence assumptions such as NPA. To enhance these methods in terms of convergence, a common underlying framework towards representing the seemingly unrelated methods is established as the up dat -ing of the distribution of the population through iterative sampling, and the methods that fit into this framework are called population distribution-based methods. Global conver-gence conditions for this framework are innovatively devel-oped by building a shadow NPA structure for the population evolution process. The result is generic and is capable of an-alyzing convergence of many methods including GA, PSO, EDA, and NPA. It can be further exploited to improve conver-gence by modifying these methods. The existing and modified variants of these methods are then applied to case studies to show the improvement. & COPY;2023 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control stra...
详细信息
Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy;detailed physics-based models provide satisfactory generalization but at the expense of high computational costs;and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble bo
暂无评论