scholarly journals A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 132 ◽  
Author(s):  
Ricardo Massao Kagami ◽  
Guinther Kovalski da Costa ◽  
Thiago Schaedler Uhlmann ◽  
Luciano Antônio Mendes ◽  
Roberto Zanetti Freire
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Open Loop ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Control Engineering ◽  
Virtual Laboratories ◽  
Interesting Approach ◽  
Reducing Costs ◽  
Remote Experimentation

In control engineering education, the possibility of using a real control system in the learning process motivates professors to improve both students’ knowledge and skills, thus avoiding an approach only based on control theory. While considering that control engineering laboratories are expensive, mainly because educational plants should reproduce classical problems that are found in the industry, the use of virtual laboratories appears as an interesting strategy for reducing costs and improving the diversity of experiments. In this research, remote experimentation was assumed regarding the ball and beam process as an alternative didactic methodology. While assuming a nonlinear and unstable open-loop process, this study presents how students should proceed to control the plant focusing on the topic that is associated with multiobjective optimization. Proportional-Integral-Derivative (PID) controller was tuned considering the Non-dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the WebLab learning procedures described in this research. The proposed strategy was compared to the Åström’s robust loop shaping method to emphasize the performance of the multiobjective optimization technique. Analyzing the feedback provided by the students, remote experimentation can be seen as an interesting approach for the future of engineering learning, once it can be directly associated with industry demand of connected machines and real-time information analysis.

Determination of Horizontal In-Situ Stress Profiles and Rock Deformation Moduli in Karamay Basin Using a Multiobjective Optimization Technique

SPE Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Hongxue Han ◽  
Maurice B. Dusseault ◽  
Shunde Yin ◽  
Guowei Xia ◽  
Mingchao Peng
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Cost Effective ◽  
In Situ Stress ◽  
Rock Deformation ◽  
Optimization Approach ◽  
Stress Profiles ◽  
Optimum Solution ◽  
In Situ Stress Measurements

Summary We introduce a quick and cost-effective method of estimating horizontal in-situ stress profiles and rock elastic moduli vs. depth from geophysical logs taken in vertical well sections. A multiobjective optimization approach finds the optimum solution for the inversion of in-situ stresses and the rock mechanical parameters from elastic borehole deformations measured by the commonly available four-arm caliper tools. The four-arm caliper log responses also permit quality control (QC) of input and identification and classification of borehole sections that display breakouts and sloughing. The method is applied in the estimation of horizontal in-situ stress profiles and rock deformation moduli vs. depth in Karamay Basin, Northwestern China. The results have demonstrated good agreement with available field in-situ stress measurements, indicating promising broader applications of the method.

Interactive evolutionary multiobjective optimization driven by robust ordinal regression

2010 ◽  
Vol 58 (3) ◽  
pp. 347-358 ◽  
Author(s):  
J. Branke ◽  
S. Greco ◽  
R. Słowiński ◽  
P. Zielniewicz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Technique ◽  
Ordinal Regression ◽  
Value Functions ◽  
Nsga Ii ◽  
Evolutionary Multiobjective Optimization ◽  
Robust Ordinal Regression ◽  
Speed Up

Interactive evolutionary multiobjective optimization driven by robust ordinal regressionThis paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.

Fault Sensitivity Analysis of Data Feedback Control Design Based on Multiobjective Optimization

2016 ◽  
Author(s):  
Young-Man Kim
Keyword(s):  
Feedback Control ◽  
Multiobjective Optimization ◽  
Estimation Error ◽  
Optimization Technique ◽  
Control Synthesis ◽  
Optimization Approach ◽  
Worst Case ◽  
Mean Square Stability ◽  
Fault Sensitivity ◽  
Data Feedback

This paper analyzes the fault sensitivity of data feedback control which is synthesized with H∞/H_ optimization technique. With I/O data, a closed-loop output predictor is parameterized by stochastically uncertain Markov parameters which are estimated by least squares. The estimation error due to bias and noise are rejected over infinite horizon and guarantees mean square stability in the sense of worst case. The measured I/O data is setup as state which makes the stability analysis and data feedback control synthesis possible. In order to improve fault sensitivity, the H_ index method is applied. Then, the controller design problem based on multiobjective optimization approach is solved in a numerically efficient way such as Linear Matrix Inequality (LMI). The fault sensitivity is analyzed in the full frequency range and its effect on the pre-defined performance is described with a simulation example.

scholarly journals A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaohui Li ◽  
Lionel Amodeo ◽  
Farouk Yalaoui ◽  
Hicham Chehade
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Parallel Machines ◽  
Experimental Results ◽  
Optimization Approach ◽  
Due Dates ◽  
Nsga Ii ◽  
Parallel Machines Scheduling ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.

scholarly journals Multipurpose Water Reservoir Management: An Evolutionary Multiobjective Optimization Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Luís A. Scola ◽  
Ricardo H. C. Takahashi ◽  
Sérgio A. A. G. Cerqueira
Keyword(s):  
Multiobjective Optimization ◽  
Flood Control ◽  
Electric Energy ◽  
Water Reservoir ◽  
Optimization Approach ◽  
Operation Planning ◽  
Nsga Ii ◽  
Evolutionary Multiobjective Optimization ◽  
Hydropower Plants ◽  
The Cost

The reservoirs that feed large hydropower plants should be managed in order to provide other uses for the water resources. Those uses include, for instance, flood control and avoidance, irrigation, navigability in the rivers, and other ones. This work presents an evolutionary multiobjective optimization approach for the study of multiple water usages in multiple interlinked reservoirs, including both power generation objectives and other objectives not related to energy generation. The classical evolutionary algorithm NSGA-II is employed as the basic multiobjective optimization machinery, being modified in order to cope with specific problem features. The case studies, which include the analysis of a problem which involves an objective of navigability on the river, are tailored in order to illustrate the usefulness of the data generated by the proposed methodology for decision-making on the problem of operation planning of multiple reservoirs with multiple usages. It is shown that it is even possible to use the generated data in order to determine the cost of any new usage of the water, in terms of the opportunity cost that can be measured on the revenues related to electric energy sales.

scholarly journals Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Qingshan Zhang ◽  
Fengyi He
Keyword(s):  
Multiobjective Optimization ◽  
Production Planning ◽  
Mass Customization ◽  
Evaluation Model ◽  
Cloud Manufacturing ◽  
Production Plan ◽  
Topsis Method ◽  
Nsga Ii ◽  
Optimal Production ◽  
Planning Scheme

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA-II algorithm. This paper established a six-tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA-II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

Tuning of Power System Stabilizers using Multiobjective Optimization NSGA II

2015 ◽  
Vol 13 (8) ◽  
pp. 2653-2660 ◽  
Author(s):  
Humberto Verdejo ◽  
Diego Gonzalez ◽  
Jose Delpiano ◽  
Cristhian Becker
Keyword(s):  
Multiobjective Optimization ◽  
Power System ◽  
Power System Stabilizers ◽  
Nsga Ii

An Evolutionary Multi-Objective Optimization Approach to the Topology Optimization of Auxetic Structures

2002 ◽  
Author(s):  
Ashraf O. Nassef
Keyword(s):  
Topology Optimization ◽  
Elastic Modulus ◽  
Finite Elements ◽  
Multiobjective Optimization ◽  
Poisson Ratio ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Finite Elements Analysis ◽  
Multi Objective ◽  
Auxetic Structures

Auxetic structures are ones, which exhibit an in-plane negative Poisson ratio behavior. Such structures can be obtained by specially designed honeycombs or by specially designed composites. The design of such honeycombs and composites has been tackled using a combination of optimization and finite elements analysis. Since, there is a tradeoff between the Poisson ratio of such structures and their elastic modulus, it might not be possible to attain a desired value for both properties simultaneously. The presented work approaches the problem using evolutionary multiobjective optimization to produce several designs rather than one. The algorithm provides the designs that lie on the tradeoff frontier between both properties.

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