检索结果-内蒙古大学图书馆

evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

EXPERT SYSTEMS WITH APPLICATIONS 2016年 51卷 120-133页

作者： Reynoso-Meza, Gilberto Sanchis, Javier Blasco, Xavier Freire, Roberto Z. Univ Politecn Valencia Inst Univ Automat & Informat Ind Camino Vera S-N E-46022 Valencia Spain Pontificia Univ Catolica Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Rua Imaculada Conceicao 1155 BR-80215901 Curitiba Parana Brazil

multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning;one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimisation Controller tuning PI tuning evolutionary multi-objective optimisation Preference handling Many-objective optimisation

来源：评论

学校读者我要写书评

暂无评论

FRAMEWORK OF AN evolutionary multi-objective optimisation METHOD FOR PLANNING A SAFE TRAJECTORY FOR A MARINE AUTONOMOUS SURFACE SHIP

引用

POLISH MARITIME RESEARCH 2019年第4期26卷 69-79页

作者： Szlapczynski, Rafal Ghaemi, Hossein Gdansk Univ Technol Narutowicza 11-12 PL-80233 Gdansk Poland

This paper represents the first stage of research into a multi-objective method of planning safe trajectories for marine autonomous surface ships (MASSs) involved in encounter situations. Our method applies an evolutionary mult-iobjective optimisation (EMO) approach to pursue three objectives: minimisation of the risk of collision, minimisation of fuel consumption due to collision avoidance manoeuvres, and minimisation of the extra time spent on collision avoidance manoeuvres. Until now, a fully multi-objective optimisation has not been applied to the real-time problem of planning safe trajectories;instead, this optimisation problem has usually been reduced to a single aggregated cost function covering all objectives. The aim is to develop a method of planning safe trajectories for MASSs that is able to simultaneously pursue the three abovementioned objectives, make decisions in real time and without interaction with a human operator, handle basic types of encounters (in open or restricted waters, and in good or restricted visibility) and guarantee compliance with the International Regulations for Preventing Collisions at Sea. It should also be mentioned that optimisation of the system based on each criterion may occur at the cost of the others, so a reasonable balance is applied here by means of a configurable trade-off This is done throughout the EMO process by means of modified Pareto dominance rules and by using a multi-criteria decision-making phase to filter the output Pareto set and choose the final solution

关键词： Maritime autonomous surface ships evolutionary multi-objective optimisation ship manoeuvres fuel consumption ship collision avoidance

来源：评论

学校读者我要写书评

暂无评论

A novel preference articulation operator for the evolutionary multi-objective optimisation of classifiers in concealed weapons detection

引用

INFORMATION SCIENCES 2015年 295卷 494-520页

作者： Rostami, Shahin O'Reilly, Dean Shenfield, Alex Bowring, Nicholas Manchester Metropolitan Univ Sch Engn Manchester M1 5GD Lancs England Sheffield Hallam Univ Dept Engn & Math Sheffield S1 1BW S Yorkshire England

The incorporation of decision maker preferences is often neglected in the evolutionary multi-objective optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the multi-objective evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of hi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for concealed weapon detection, producing better results than previously published classifier implementations. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Preference articulation Concealed weapon detection evolutionary multi-objective optimisation Decision making

来源：评论

学校读者我要写书评

暂无评论

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

引用

CONTROL ENGINEERING PRACTICE 2014年第1期28卷 58-73页

作者： Reynoso-Meza, Gilberto Blasco, Xavier Sanchis, Javier Martinez, Miguel Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia 46022 Spain

Control engineering problems are generally multi-objective problems;meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions;the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： evolutionary multi-objective optimisation multi-objective evolutionary algorithms multi-criteria decision making multi-objective optimisation design procedure Controller tuning

来源：评论

学校读者我要写书评

暂无评论

Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods

引用

SWARM AND evolutionary COMPUTATION 2020年 54卷 100652-000页

作者： Cruz-Reyes, Laura Fernandez, Eduardo Patricia Sanchez-Solis, J. Coello Coello, Carlos A. Gomez, Claudia Natl Mexican Inst Technol Postgrad & Res Div Madero Inst Technol Madero 89440 Tamaulipas Mexico Univ Politecn Mar & Sierra La Cruz 82740 Sinaloa Mexico Autonomous Univ Ciudad Juarez Dept Elect Engn & Comp Ciudad Juarez 32310 Chihuahua Mexico CINVESTAV IPN Comp Sci Dept Mexico City 07360 DF Mexico

A large number of real-world problems require optimising several objective functions at the same time, which are generally in conflict. Many of these problems have been addressed through multi-objective evolutionary algorithms. In this paper, we propose a new hybrid evolutionary algorithm whose main feature is the incorporation of the Decision Maker's (DM's) preferences through multi-criteria ordinal classification methods in early stages of the optimisation process, being progressively updated. This increases the selective pressure towards the privileged zone of the Pareto front more in agreement with the DM's preferences. An extensive experimental research was conducted to answer three main questions: i) to what extent the proposal improves the convergence towards the region of interest for the DM;ii) to what extent the proposal becomes more relevant as the number of objectives increases, and iii) to what extent the effectiveness of the hybrid algorithm depends on the particular multi-criteria method used to assign solutions to ordered classes. The issues used to evaluate our proposal and answer the questions were seven scalable test problems from the DTLZ test suite and some instances of project portfolio optimisation problems, with three and eight objectives. Compared to MOEA/D and MOEA/D-DE, the results showed that the proposed strategy obtains a better convergence towards the region of interest for the DM and also performs better characterisation of that zone on a wide range of objective functions.

关键词： evolutionary multi-objective optimisation multi-criteria ordinal classification Preference incorporation

来源：评论

学校读者我要写书评

暂无评论

Computational steering of a multi-objective evolutionary algorithm for engineering design

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2007年第8期20卷 1047-1057页

作者： Shenfield, Alex Fleming, Peter J. Alkarouri, Muhammad Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

The execution process of an evolutionary algorithm typically involves some trial and error. This is due to the difficulty in setting the initial parameters of the algorithm-especially when little is known about the problem domain. This problem is magnified when applied to many-objective optimisation, as care is needed to ensure that the final population of candidate solutions is representative of the trade-off surface. We propose a computational steering system that allows the engineer to interact with the optimisation routine during execution. This interaction can be as simple as monitoring the values of some parameters during the execution process, or could involve altering those parameters to influence the quality of the solutions produced by the optimisation process. The implementation of this steering system should provide the ability to tailor the client to the hardware available, for example providing a lightweight steering and visualisation client for use on a PDA. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： computational steering evolutionary multi-objective optimisation decision making engineering design interaction visualisation

来源：评论

学校读者我要写书评

暂无评论

Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications

引用

INTERNATIONAL JOURNAL OF MATERIAL FORMING 2015年第3期8卷 469-480页

作者： Hamdaoui, Mohamed Oujebbour, Fatima-Zahra Habbal, Abderrahmane Breitkopf, Piotr Villon, Pierre Univ Lorraine UMR 7239 LEM3 F-57045 Metz 01 France Univ Nice Sophia Antipolis INRIA Projet OPALE 2004 F-06902 Sophia Antipolis France UTC CNRS Lab Roberval UMR 7337 F-60205 Compiegne France

The aim of this paper is to present a method to perform evolutionary multi-objective optimization of CPU intensive sheet metal forming applications using kriging surrogates. Two main ingredients are employed to achieve this goal. First of all, given a learning dataset, the kriging surrogate is designed to minimize the leave-one-out error. Secondly, during the optimization, new data points are added to the learning set to update the surrogate locally (by well chosen points on the current Pareto front) and globally (by maximum kriging variance points over the entire design landscape). The ability of the method to capture Pareto fronts with accuracy is demonstrated on the well-known ZDT test functions. The method is then tested on a real-life problem, the simultaneous minimization of springback and failure for a three-dimensional CPU intensive high strength steel stamping industrial use case.

关键词： Kriging evolutionary multi-objective optimisation NSGA-II Sheet metal forming Springback Failure

来源：评论

学校读者我要写书评

暂无评论

Design of hybrid distillation/melt crystallisation processes for separation of close boiling mixtures

引用

CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2013年 67卷 16-24页

作者： Micovic, Jovana Beierling, Thorsten Lutze, Philip Sadowski, Gabriele Gorak, Andrzej TU Dortmund Univ Dept Biochem & Chem Engn Lab Fluid Separat D-44227 Dortmund Germany TU Dortmund Univ Dept Biochem & Chem Engn Lab Thermodynam D-44227 Dortmund Germany Tech Univ Lodz Fac Environm & Proc Engn PL-90924 Lodz Poland

Hybrid separations combining distillations and crystallisations have a significant potential for process intensification. To address the large number of degrees of freedom in the design of hybrid separations, a three-step approach is utilised. However, it can only be applied if all parameters for the rigorous modelling of crystallisation and cost functions are known a priori, which is often not the case. In this paper, we propose a four-step design method which can be applied in early process development stages when not all model parameters are available. In the first step, different process variants are generated. In the second step, the variants are evaluated using rigorous models, wherein the unknown model parameters are varied to quantify their influence on the process performance. If hybrid separations appear to be compatible, experiments are performed to determine the unknown parameters in the third step. In the last step, an optimisation is performed to find the optimal process, when necessary in dependence of unknown cost parameters. The developed tools and the feasibility of the approach are illustrated with the separation of a binary mixture of long-chain isomeric aldehydes. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Hybrid separations Melt crystallisation Modelling evolutionary multi-objective optimisation Long chain isomeric aldehydes

来源：评论

学校读者我要写书评

暂无评论

Improving many objective optimisation algorithms using objective dimensionality reduction

引用

evolutionary INTELLIGENCE 2020年第3期13卷 365-380页

作者： Nguyen, Xuan Hung Bui, Lam Thu Tran, Cao Truong Le Quy Don Tech Univ Res Grp Computat Intelligence 236 Hoang Quoc Viet St Hanoi Vietnam

Many-objective optimisation problems (MaOPs) have recently received a considerable attention from researchers. Due to the large number of objectives, MaOPs bring serious difficulties to existing multi-objective evolutionary algorithms (MOEAs). The major difficulties includes the poor scalability, the high computational cost and the difficulty in visualisation. A number of many-objective evolutionary algorithms (MaOEAs) has been proposed to tackle MaOPs, but existing MaOEAs have still faced with the difficulties when the number of objectives increases. Real-world MaOPs often have redundant objectives that are not only inessential to describe the Pareto-optimal front, but also deteriorate MaOEAs. A common approach to the problem is to use objective dimensionality reduction algorithms to eliminate redundant objectives. By removing redundant objectives, objective reduction algorithms can improve the search efficiency, reduce computational cost, and support for decision making. The performance of an objective dimensionality reduction strongly depends on nondominated solutions generated by MOEAs/MaOEAs. The impact of objective reduction algorithms on MOEAs and vice versa have been widely investigated. However, the impact of objective reduction algorithms on MaOEAs and vice versa have been rarely investigated. This paper studies the interdependence of objective reduction algorithms on MaOEAs. Experimental results show that combining an objective reduction algorithm with an MOEA can only successfully remove redundant objectives when the total number of objectives is small. In contrast, combining the objective reduction algorithm with an MaOEA can successfully remove redundant objectives even when the total number of objectives is large. Experimental results also show that objective reduction algorithms can significantly improve the performance of MaOEAs.

关键词： evolutionary multi-objective optimisation Many-objective optimisation objective dimensionality reduction Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Decision Support Systems Using Ensemble Genetic Programming

引用

JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2006年第4期5卷 303-313页

作者： Abraham, Ajith Grosan, Crina Yonsei Univ Sch Comp Sci IITA Professorship Program 134 Shinchon Dong Seoul 120749 South Korea Univ Babes Bolyai Dept Comp Sci Cluj Napoca Romania

This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.

关键词： Decision support systems genetic programming ensemble systems evolutionary multi-objective optimisation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：