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A Kriging Based Forecasting and Scheduling System for Scientific Computing Cloud Applications

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第3期8卷 227-244页

作者： Li, Zhaojun Wang, Xinyu Li, Zheng Wang, Xicheng Li, Keqiu Dalian Univ Technol Sch Comp Sci & Technol Dalian Liaoning Provin Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian Liaoning Provin Peoples R China

Regarding to the theories and techniques of cloud computing having been developed and applied in scientific computing field, tasks can be conveniently managed by the cloud platform on the basis of standardized scheduling system with cost (resources consumed) recorded. However, there are two issues which drag the customers' attention: 1) When will the tasks expect for termination (response time) under a specific resource scheduling;2) What is the best scheduling solution by considering cost. In order to reply these two questions, a Kriging based forecasting and scheduling system has been proposed in this paper. With the cooperation between the scientific designer and the cloud designer, the design variables for evaluating the cloud applications can be achieved;Kriging surrogate model is then introduced to simulate the approximate functional relationship between the design variables and the response time of the tasks;sequential quadratic programming optimization algorithm then provides the best scheduling solution for the tasks if cost constraints are to be met. Two real scientific computing cloud applications have been testified on an OpenStack cloud platform, with consequences described in details. The work in this paper has put forward a novel way for the designers and the customers on predictable and reasonable scheduling strategies for the various resource-intensive scientific computing cloud applications with surrogate models and optimization algorithms.

关键词： Resource-intensive scientific computing Cloud computing task scheduling Kriging sequential quadratic programming OpenStack

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Comment on "Hybrid DE-SQP algorithm for non-convex short term hydrothermal scheduling problem" by S. Sivasubramani and K. Shanti Swarup [Energy Conyers. Manage. 52 (2011) 757-761]

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ENERGY CONVERSION AND MANAGEMENT 2015年 106卷 1471-1473页

作者： Ahmadi, Mohammad Reza Islamic Azad Univ Janah Branch Dept Civil Engn Janah Iran

This paper aims to analyze the inaccuracies occurred in "Hybrid DE-SQP algorithm for non-convex short term hydrothermal scheduling problem" by S. Sivasubramani and K. Shanti Swarup [Energy Conyers. Manage. 52 (2011) 757-761]. The original paper Sivasubramani and Shanti Swarup (2011) has proposed the differential evolution (DE) and sequential quadratic programming (SQP) method to solve the non-convex short term hydrothermal scheduling problem, but the offered solution is not satisfactory, while considering the constraints of reservoir storage volume, the ultimate reservoir storage volumes of hydro-units and the power produced by such units. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Differential evolution sequential quadratic programming Hydrothermal system

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Maximizing Demineralization during Chemical Leaching of Coal through Optimal Reagent Addition Policy

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CHEMICAL PRODUCT AND PROCESS MODELING 2015年第1期10卷 1-9页

作者： Dash, Pratik S. Prasad, D. N. Sriramoju, Santosh K. Lingam, R. K. Suresh, A. Banerjee, P. K. Ganguly, S. Tata Steel R&D Jamshedpur 831001 Jharkhand India IIT Dept Chem Engn Kharagpur W Bengal India

The main objective of the optimal reagent addition was to maximize the quantity of product with minimal quantity of feed. In the present study, the optimal addition of reagents during the chemical leaching of coal was computed. Chemical leaching of coal was carried out using aqueous solution of caustic to dissolve and remove the mineral matter. Simulation studies were carried out using the optimal reagent addition for chemical leaching of coal in batch reactors. This was experimentally validated, using the bench-scale reactor setup with hierarchical optimization architecture. Chemical leaching experiments were conducted using West Bokaro coal. Samples collected at various time intervals during the experiment were analyzed. Variations in silica (SiO2) and alumina (Al2O3) concentrations, which were main constituents present in coal ash, were evaluated with respect to time for different concentrations of caustic. The simulation studies for optimal addition were carried out at 6, 8 and 10 intervals. An objective function, required for maximum ash removal, was solved, using sequential quadratic programming (SQP) algorithm to find out the optimum sequence for reagent dosing. An improvement of about 1% (wt) ash reduction on an average was observed with implementation of optimal reagent addition.

关键词： coal optimization leaching mineral processing sequential quadratic programming

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Optimal Placement of Phasor Measurement Units with Linear and Non-linear Models (vol 43, pg 357, 2015)

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第6期43卷 721-721页

作者： Nikolaos, Theodorakatos Korres, George Manousakis, Nikolaos ECE Department National Technical University of Athens Athens Greece

—This article presents a non-linear programming-based model for the optimal placement of phasor measurement units. The optimal phasor measurement units placement is formulated to minimize the number of phasor measurement units required for full system observability and to maximize the measurement redundancy at all buses in a power system. A sequential quadratic programming algorithm is used for the solution of the proposed model. The existence of power flow and injection measurements, the limited phasor measurement units channel capacity, the lack of communication facilities in substations, and the single phasor measurement units loss are also incorporated into the initial proposed formulation. The non-linear programming model is applied to IEEE 14- and 118-bus test systems in MATLAB. The accuracy and the effectiveness of the proposed method is verified by comparing the simulation results to those obtained by a binary integer programming model also implemented in MATLAB. The comparative study shows that the proposed non-linear programming model yields the same number of phasor measurement units as the binary integer programming model. A remarkable advantage of the non-linear programming against binary integer linear programming is its capability to give more than one optimal solution, each one having the same minimum number of phasor measurement units (same minimum objective value), but at different locations.

关键词： Phasor measurement unit observability optimal placement optimization binary integer linear programming mixed integer linear programming non-linear programming sequential quadratic programming

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An Extended sequential quadratically Constrained quadratic programming Algorithm for Nonlinear, Semidefinite, and Second-Order Cone programming

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2013年第2期156卷 183-212页

作者： Auslender, Alfred Univ Lyon 1 Inst Camille Jordan F-69365 Lyon France Ecole Polytech Dept Econ Palaiseau France

This paper is concerned with nonlinear, semidefinite, and second-order cone programs. A general algorithm, which includes sequential quadratic programming and sequential quadratically constrained quadratic programming methods, is presented for solving these problems. In the particular case of standard nonlinear programs, the algorithm can be interpreted as a prox-regularization of the Solodov sequential quadratically constrained quadratic programming method presented in Mathematics of Operations Research (2004). For such type of methods, the main cost of computation amounts to solve a linear cone program for which efficient solvers are available. Usually, "global convergence results" for these methods require, as for the Solodov method, the boundedness of the primal sequence generated by the algorithm. The other purpose of this paper is to establish global convergence results without boundedness assumptions on any of the iterative sequences built by the algorithm.

关键词： sequential quadratic programming sequential quadratically constrained quadratic programming Nonlinear programming Semidefinite programming Second-order cone programming Global convergence

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Hybridization of Artificial Immune Systems and sequential quadratic programming for Dynamic Economic Dispatch

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2009年第9期37卷 1036-1045页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so as to operate an electric power system most economically. This article proposes a hybrid methodology integrating artificial immune systems with sequential quadratic programming for solving the dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates artificial immune systems as a base level search, which can give good direction to the optimal region and sequential quadratic programming as a local search procedure, which is used to fine tune that region for achieving the final solution. Numerical results of a ten-unit system have been presented to demonstrate the performance and applicability of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from a hybrid of particle swarm optimization and sequential quadratic programming and a hybrid of evolutionary programming and sequential quadratic programming.

关键词： dynamic economic dispatch artificial immune systems sequential quadratic programming

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A New Design of Double Input Power System Stabilizers Using SQP for Interconnected Power Systems

A New Design of Double Input Power System Stabilizers Using ...

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15th International Conference on Modern Electric Power Systems (MEPS)

作者： Tavakoli, Mohamad Reza Rasouli, Vahid Allahkaram, Sahar Univ Isfahan Dept Elect Engn Esfahan Iran Islamic Azad Univ Islamshahr Branch Dept Elect Engn Islamshahr Iran

ISBN: (纸本)9781509031016

Double input Power system stabilizers (PSSs) are recently used in power system to damp out low frequency oscillations. This paper presents a new design for PSSs with dual inputs (PSS2B, PSS3B, PSS4B) using sequential quadratic programming (SQP). The proposed method is simple, effective and can guarantee that the overall system performance is desirable. The simulation results using single machine and multi machine power systems show that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS4B offers superior transient performance. Comparative results of the proposed SQP method and other intelligent methods, PSO, GA and BFA, reveal that the new proposed control strategy has a better robustness and performance over other existing techniques.

关键词： dual input power system stabilizer low frequency oscillations sequential quadratic programming power system stability

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A Decomposition and Coordination-Based Method for Chiller Plant Optimization

A Decomposition and Coordination-Based Method for Chiller Pl...

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IEEE International Conference on Complex Systems Engineering (ICCSE)

作者： Zhang, Danxu Luh, Peter B. Fan, James Gupta, Shalabh

ISBN: (纸本)9781467371780

Since chiller plants consume a large portion of the energy use in the buildings with HVAC systems, growing attention has been paid to chiller plant optimization. A typical chiller plant includes devices such as chillers, cooling towers, primary pumps and condenser pumps. Operation settings of a device are zeros when the device is off;otherwise, they are within positive ranges. With nonlinearity in heat exchange, chiller plant optimization is a mixed-integer nonlinear problem. Establishing a problem formulation with a good balance between accuracy and simplicity is usually difficult. For example, water temperature is critical in improving chiller efficiency while chiller power consumption is a highly nonlinear function of the temperature. In this paper, for simplicity, devices of the same class are assumed identical. To save energy and simplify the problem, a formulation with chilled water supply temperature as an additional decision variable and condenser water supply temperature as a parameter is established. To obtain good solutions and save computational effort, surrogate Lagrangian relaxation (SLR) combined with sequential quadratic programming (SQP) with good initialization is used. Numerical testing shows that power consumption is significantly reduced with maximum savings around 18% in a partial load condition by using our method as compared with a baseline using current strategies.

关键词： chiller plant optimization surrogate Lagrangian relaxation sequential quadratic programming

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Using a Filter-Based sequential quadratic programming Algorithm in a Parallel Environment

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JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2009年第12期6卷 635-648页

作者： Venter, Gerhard Vanderplaats, Garret N. Univ Stellenbosch Dept Mech & Mech Engn ZA-7602 Stellenbosch South Africa Vanderplaats Res & Dev Inc Monterey CA 93940 USA

A parallel, filter-based, sequential quadratic programming algorithm is implemented and tested for typical general-purpose engineering applications. Constrained engineering test problems, including a finite element simulation, with up to 512 design variables are considered. The accuracy and serial performance of the filter-based algorithm are compared against that of a standard sequential quadratic programming algorithm. The parallel performance of the algorithm is evaluated, using up to 52 cores on a Linux Cluster. The results indicate that the filter-based algorithm competes favorably with a standard sequential quadratic programming algorithm in a serial environment. However, the filter-based algorithm exhibits much better parallel efficiency due to the lack of a one-dimensional search.

关键词： sequential quadratic programming Parallel Computing Finite Element Analysis sequential Linear programming High Performance Computing Particle Swarm Optimization Nonlinear programming Numerical Simulation Parallel Algorithm Young's Modulus

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Dual decomposition for QPs in scenario tree NMPC

Dual decomposition for QPs in scenario tree NMPC

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European Control Conference (ECC)

作者： Leidereiter, Conrad Potschka, Andreas Bock, Hans Georg Heidelberg Univ Interdisciplinary Ctr Sci Comp Neuenheimer Feld 368 D-69120 Heidelberg Germany

ISBN: (纸本)9783952426937

In the field of nonlinear model predictive control (NMPC) under uncertainty we use a robustification approach based on a scenario tree formulation. This approach is known to be less conservative than worst-case approaches. A main challenge of scenario tree NMPC in high-dimensional uncertainty spaces is the exponential growth of the number of scenarios for possible parameter realizations. Hence, the solution of the resulting optimization problem in every NMPC iteration becomes a bottleneck for the computation. We have to solve the problem with fast numerical methods to ensure real-time applicability of scenario tree NMPC. By a multiple shooting discretization we discretize the optimal control problems of an NMPC iteration to obtain a sequence of Nonlinear Programs that we solve by a real-time feasible variant of sequential quadratic programming (SQP). Every single quadratic programming problem (QP) exhibits a particular structure originating from the scenario tree. We use a dual decomposition approach on the non-anticipativity constraints from the scenario tree formulation to decouple the large-scale QP into many smaller QPs that each correspond to one scenario in the scenario tree. Within an outer level non-smooth Newton iteration in the dual space of the coupling constraints, the decoupled scenario QPs can be solved in a massively parallel fashion. In the final part of this contribution we present numerical results for NMPC with large scenario trees.

关键词： Newton method nonlinear control systems nonlinear programming optimal control predictive control quadratic programming robust control SQP coupling constraints dual decomposition high-dimensional uncertainty spaces multiple shooting discretization nonanticipativity constraints nonlinear model predictive control nonlinear program optimal control problems optimization problem outer level nonsmooth Newton iteration robustification approach scenario tree NMPC sequential quadratic programming worst-case approach Couplings Indexes Optimal control Optimization Real-time systems Robustness Uncertainty

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