scholarly journals Approach to robust multi-objective optimization and probabilistic analysis: the ROPAR algorithm

2019 ◽  
Vol 21 (3) ◽  
pp. 427-440 ◽  
Author(s):  
Oscar O. Marquez-Calvo ◽  
Dimitri P. Solomatine
Keyword(s):  
Robust Optimization ◽  
Probabilistic Analysis ◽  
Drainage System ◽  
Multi Objective Optimization ◽  
Simulation Framework ◽  
Computing Environment ◽  
Rainfall Events ◽  
Multi Objective ◽  
Wide Range ◽  
Storm Drainage

Abstract This paper considers the problem of robust optimization, and presents the technique called Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). It has been developed for finding robust optimum solutions of a particular class in model-based multi-objective optimization (MOO) problems (i.e. when the objective function is not known analytically), where some of the parameters or inputs to this model are assumed to be uncertain. A Monte Carlo simulation framework is used. It can be straightforwardly implemented in a distributed computing environment which allows the results to be obtained relatively fast. The technique is exemplified in the two case studies: (a) a benchmark problem commonly used to test MOO algorithms (a version of the ZDT1 function); and (b) a design problem of a simple storm drainage system, where the uncertainty is associated with design rainfall events. It is shown that the design found by ROPAR can adequately cope with these uncertainties. The approach can be useful for assisting in a wide range of risk-based decisions.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

Toward the Use of Pareto Performance Solutions and Pareto Robustness Solutions for Multi-Objective Robust Optimization Problems

2012 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Oscar Brito Augusto
Keyword(s):  
Robust Optimization ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Robust Solutions ◽  
Multi Objective ◽  
Robust Optimization Problem ◽  
Design Solutions ◽  
Further Development

For Multi-Objective Robust Optimization Problem (MOROP), it is important to obtain design solutions that are both optimal and robust. To find these solutions, usually, the designer need to set a threshold of the variation of Performance Functions (PFs) before optimization, or add the effects of uncertainties on the original PFs to generate a new Pareto robust front. In this paper, we divide a MOROP into two Multi-Objective Optimization Problems (MOOPs). One is the original MOOP, another one is that we take the Robustness Functions (RFs), robust counterparts of the original PFs, as optimization objectives. After solving these two MOOPs separately, two sets of solutions come out, namely the Pareto Performance Solutions (PP) and the Pareto Robustness Solutions (PR). Make a further development on these two sets, we can get two types of solutions, namely the Pareto Robustness Solutions among the Pareto Performance Solutions (PR(PP)), and the Pareto Performance Solutions among the Pareto Robustness Solutions (PP(PR)). Further more, the intersection of PR(PP) and PP(PR) can represent the intersection of PR and PP well. Then the designer can choose good solutions by comparing the results of PR(PP) and PP(PR). Thanks to this method, we can find out the optimal and robust solutions without setting the threshold of the variation of PFs nor losing the initial Pareto front. Finally, an illustrative example highlights the contributions of the paper.

scholarly journals Data cache optimization model based on cyclic genetic ant colony algorithm in edge computing environment

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986786
Author(s):  
Danyue Wang ◽  
Xingshuo An ◽  
Xianwei Zhou ◽  
Xing Lü
Keyword(s):  
Ant Colony Algorithm ◽  
Response Speed ◽  
Data Access ◽  
Edge Computing ◽  
Multi Objective Optimization ◽  
Computing Environment ◽  
Data Caching ◽  
Multi Objective ◽  
Access Frequency ◽  
Cache Hit Ratio

Edge computing has recently emerged as an important paradigm to bring filtering, processing, and caching resources to the edge of networks. However, with the increasing popularity of augmented reality and virtual reality application, user requirements on data access speed have increased. Since the edge node has limited cache space, efficient data caching model is needed to improve the performance of edge computing. We propose a multi-objective optimization data caching model in the edge computing environment using data access counts, data access frequency, and data size as optimization goals. Our model differs from previous data caching schemes that focused only on data access counts or data size. In addition, a cyclic genetic ant algorithm is proposed to solve the multi-objective optimization data caching model. We compare the following three performance indicators: cache hit ratio, average response speed, and bandwidth cost. Simulation results show that the model can improve the cache hit ratio and reduce the response latency and the bandwidth cost.

Use Multi-Objective Optimization for Designing Cloaks With Acoustic and Elastic Materials

2012 ◽  
Author(s):  
Chunyan Bao ◽  
Liang-Wu Cai
Keyword(s):  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Frequency Range ◽  
Elastic Materials ◽  
Optimization Scheme ◽  
Multi Objective ◽  
Wide Range ◽  
Optimization Schemes ◽  
Elastic Layers

The various optimization schemes have been recently shown to be a capable tool for designing acoustic cloaks based on Cummer-Schurig prescription without requiring material singularity. However, when using an optimization scheme to optimize a cloak at one specified frequency, sometimes it is observed that the cloaking effect deteriorate at other frequencies. This paper explores the use of multi-objective optimization approach such that the cloaking performance is maintained over a wide range of frequencies. Two examples are presented. The first cloak is comprised of all aocustic layers, and the second cloak is comprised of a mixture of acoustic and elastic layers. The results show the effectiveness of the multi-objective optimization approach on maintaining cloaking performance over a specified frequency range.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Optimization Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

scholarly journals Generalized active disturbance rejection control for the boiler-turbine unit using multi-objective optimization and extended state observer

2019 ◽  
Vol 272 ◽  
pp. 01002
Author(s):  
Jianzhong Zhu ◽  
Xiao Wu ◽  
Jiong Shen ◽  
Meihong Wang
Keyword(s):  
Control Strategy ◽  
Disturbance Rejection ◽  
Turbine Unit ◽  
Extended State Observer ◽  
Fuel Quality ◽  
Multi Objective Optimization ◽  
Extended State ◽  
Multi Objective ◽  
Active Disturbance Rejection ◽  
Wide Range

This paper proposes a generalized active disturbance rejection controller (GADRC) based hierarchical control structure for the boilerturbine unit. In the lower layer, a multivariable extended state observer (MESO) is developed to estimate the values of the lumped disturbances caused by modelling mismatches, fuel quality variation and wide range load variation. The influence of the disturbances is then compensated at the input side as a feedforward control. In the upper layer, the multi-objective optimization is devised to obtain the set-points by removing the plant behaviour variation from the optimized model in a feasible way. The lowpass filter acting on the lumped disturbances is designed to bridge the gap between the lower and upper layer. The impact of the feedthrough item is approximated by a first-order system and a two degree-of-freedom (2-DOF) control strategy is established to illustrate the set-point tracking and disturbance rejection properties of the controller. Simulation studies on a 1000MWe coal-fired ultra-supercritical boiler-turbine unit demonstrate that the proposed control strategy can achieve a satisfactory performance in cases of fuel quality variations, model-plant mismatches and wide range load variation.

scholarly journals Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty

2019 ◽  
Vol 9 (10) ◽  
pp. 2151
Author(s):  
Pengzhi Wei ◽  
Yanqiu Li ◽  
Tie Li ◽  
Naiyuan Sheng ◽  
Enze Li ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Process Window ◽  
Sensitivity Factor ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Lithography Process ◽  
Wide Range ◽  
Focus Plane ◽  
Technology Nodes ◽  
First Time

The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable and uncertain in the real exposure process. But conventional SMO assumes the lithography system is ideal, which only compensates the optical proximity effect (OPE) in the best focus plane. Therefore, to solve the inverse lithography problem with more uniformity of pattern in different defocus variations, we proposed a defocus robust SMO (DRSMO) approach that is driven by a defocus sensitivity penalty function for the first time. This multi-objective optimization samples a wide range of defocus disturbances and it can be proceeded by the mini-batch gradient descent (MBGD) algorithm effectively. The simulation results showed that a more robust defocus source and mask can be designed through DRSMO optimization. The defocus sensitivity factor sβ maximally decreased 63.5% compared to conventional SMO, and due to the low error sensitivity and the depth of defocus (DOF), the process window (PW) was further enlarged effectively. Compared to conventional SMO, the exposure latitude (EL) maximally increased from 4.5% to 10.5% and DOF maximally increased 54.5% (EL = 5%), which proved the validity of the DRSMO method in improving the focusing performance.

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