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3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Moawad, Assaad Hartmann, Thomas Fouquet, Francois Nain, Gregory Klein, Jacques Bourcier, Johann Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Univ Rennes 1 IRISA INRIA Rennes France

ISBN: (纸本)9789897581366

multi-objective evolutionary algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge. Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations.

关键词： multi-objective evolutionary algorithms Optimization Genetic algorithms Model-driven Engineering

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multi-objective Design Optimization of Three-Phase Induction Motor Using NSGA-II Algorithm 1

Multi-objective Design Optimization of Three-Phase Induction...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Ranjan, Soumya Mishra, Sudhansu Kumar NIST Dept Elect & Elect Engn Berhampur Orissa India Birla Inst Technol Dept Elect & Elect Engn Ranchi Bihar India

ISBN: (纸本)9788132222088;9788132222071

The modeling of electrical machine is approached as a system optimization, more than a simple machine sizing. Hence wide variety of designs are available and the task of comparing the different options can be very difficult. A number of parameters are involved in the design optimization of the induction motor and the performance relationship between the parameters also is implicit. In this paper, a multi-objective problem is considered in which three phase squirrel cage induction motor (SCIM) has been designed subject to the efficiency and power density as objectives. The former is maximized where the latter is minimized simultaneously considering various constraints. Three single objective methods such as Tabu Search (TS), Simulated Annealing (SA) and Genetic Algorithm (GA) is used for comparing the Pareto solutions. Performance comparison of techniques is done by performing different numerical experiments. The result shows that NSGA-II outperforms other three for the considered test cases.

关键词： multi-objective optimization Induction motors multi-objective evolutionary algorithms Single objective evolutionary algorithm

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multi-objective Optimization of Gate Location and Processing Conditions in Injection Molding Using MOEAs: Experimental Assessment 8

Multi-Objective Optimization of Gate Location and Processing...

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8th International Conference on evolutionary multi-Criterion Optimization (EMO)

作者： Fernandes, Celio Pontes, Antonio J. Viana, Julio C. Gaspar-Cunha, Antonio Univ Minho Dept Polymer Engn Inst Polymer & Composites I3N Braga Portugal

ISBN: (纸本)9783319158921;9783319158914

The definition of the gate location in injection molding is one of the most important factors in achieving dimensionally accuracy of the parts. This paper presents an optimization methodology for addressing this problem based on a multi-objective evolutionary Algorithm (MOEA). The algorithm adopted here is named Reduced Pareto Set Genetic Algorithm (RPSGA) and was used to create a balanced filling pattern using weld line characterization. The optimization approach proposed in this paper is an integration of evolutionary algorithms with Computer-Aided Engineering (CAE) software (Autodesk Moldflow Plastics software). The performance of the proposed optimization methodology was illustrated with an example consisting in the injection of a rectangular part with a non-symmetrical hole. The numerical results were experimentally assessed. Physical meaning was obtained which guaranteed a successful process optimization.

关键词： multi-objective evolutionary algorithms Gate Location Moldflow

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Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1843-1854页

作者： Li, Zhiyong Rudolph, Guenter Li, Kenli Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Univ Dortmund Lehrstuhl Algorithm Engn D-44221 Dortmund Germany

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H-epsilon gate and R&N-epsilon gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H-epsilon gate and R&N-epsilon gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N-epsilon gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate E value regions for two Q-gates are verified. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms multi-objective 0/1 knapsack problems Quantum computing Convergence performance

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Extracting Variability-Safe Feature Models from Source Code Dependencies in System Variants 15

Extracting Variability-Safe Feature Models from Source Code ...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Assuncao, Wesley K. G. Lopez-Herrejon, Roberto E. Linsbauer, Lukas Vergilio, Silvia R. Egyed, Alexander Univ Fed Parana DINF BR-81531980 Curitiba Parana Brazil Technol Fed Univ Parana COINF BR-85902490 Toledo Brazil Johannes Kepler Univ Linz ISSE A-4040 Linz Austria

ISBN: (纸本)9781450334723

To effectively cope with increasing customization demands, companies that have developed variants of software systems are faced with the challenge of consolidating all the variants into a Software Product Line, a proven development paradigm capable of handling such demands. A crucial step in this challenge is to reverse engineer feature models that capture all the required feature combinations of each system variant. Current research has explored this task using propositional logic, natural language, and search-based techniques. However, using knowledge from the implementation artifacts for the reverse engineering task has not been studied. We propose a multi-objective approach that not only uses standard precision and recall metrics for the combinations of features but that also considers variability-safety, i.e. the property that, based on structural dependencies among elements of implementation artifacts, asserts whether all feature combinations of a feature model are in fact well-formed software systems. We evaluate our approach with five case studies and highlight its bene fits for the software engineer.

关键词： Reverse Engineering Feature Models multi-objective evolutionary algorithms

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Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems

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FUZZY SETS AND SYSTEMS 2009年第15期160卷 2192-2205页

作者： Lopez-Herrera, A. G. Herrera-Viedma, E. Herrera, F. Univ Granada Dept Comp Sci & AI E-18071 Granada Spain

The performance of information retrieval systems (IRSs) is Usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant documents retrieved by the IRS in response to a user's query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents retrieved to the total number of relevant documents for the user's query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended Boolean queries are used, defining the user's queries in a manual way is usually a complex task. In this contribution. our interest is focused on the automatic learning of extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting genetic a algorithm (NSGA-II) and the Strength Pareto evolutionary algorithm (SPEA2). (C) 2009 Elsevier B.V. All rights reserved.

关键词： Information retrieval systems Genetic programming Inductive query by example multi-objective evolutionary algorithms Query learning

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DMEA-II: the direction-based multi-objective evolutionary algorithm-II

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SOFT COMPUTING 2014年第11期18卷 2119-2134页

作者： Long Nguyen Bui, Lam T. Abbass, Hussein A. Le Quy Don Tech Univ Fac Informat Technol Hanoi Vietnam Univ New S Wales Australian Def Force Acad Canberra ACT Australia

This paper discusses the use of direction of improvement in guiding multi-objective evolutionary algorithms (MOEAs) during the search process towards the area of Pareto optimal set. We particularly propose a new version of the Direction based multi-objective evolutionary Algorithm (DMEA) and name it as DMEA-II. The new features of DMEA-II includes (1) an adaptation of the balance between convergence and spreading by using an adaptive ratio between the convergence and spreading directions being selected over time;(2) a new concept of ray-based density for niching;and (3) a new selection scheme based on the ray-based density for selecting solutions for the next generation. To validate the performance of DMEA-II, we carried out a case study on a wide range of test problems and comparison with other MOEAs. It obtained quite good results on primary performance metrics, namely the generation distance, inverse generation distance, hypervolume and the coverage set. Our analysis on the results indicates the better performance of DMEA-II in comparison with the most popular MOEAs.

关键词： multi-objective evolutionary algorithms Direction-based MOEAs Performance measurement DMEA

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multiobjective evolutionary algorithm with a discrete differential mutation operator developed for service restoration in distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 700-711页

作者： Sanches, Danilo Sipoli London, Joao Bosco A., Jr. Delbem, Alexandre Claudio B. Prado, Ricardo S. Guimaraes, Frederico G. Neto, Oriane M. de Lima, Telma W. Fed Technol Univ Parana Cornelia Procopio Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil Fed Inst Minas Gerais Ouro Preto Brazil Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Univ Fed Goias Inst Informat Goiania Go Brazil

Network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objective functions. For large scale distribution systems no exact algorithm has found adequate restoration plans in real-time. On the other hand, the combination of multi-objective evolutionary algorithms (MOEAs) with the Node-Depth Encoding (NDE) has been able to efficiently generate adequate restoration plans for relatively large distribution systems (with thousands of buses and switches). The approach called MEAN results from the combination of NDE with a technique of MOEA based on subpopulation tables. In order to improve the capacity of MEAN to explore both the search and objective spaces, this paper proposes a new approach that results from the combination of MEAN with characteristics from the mutation operator of the Differential Evolution (DE) algorithm. Simulation results have shown that the proposed approach, called MEAN-DE, is able to find adequate restoration plans for distribution systems from 3860 to 30,880 switches. Comparisons have been performed using the Hypervolume metric and the Wilcoxon rank-sum test. In addition, a MOEA using subproblem Decomposition and NDE (MOEA/D-NDE) was investigated. MEAN-DE has shown the best average results in relation to MEAN and MOEA/D-NDE. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Large-Scale Distribution Systems Service restoration Node-Depth Encoding multi-objective evolutionary algorithms Differential Evolution

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multi-objective evolutionary Algorithm for single and multiple fault service restoration in large-scale distribution systems

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ELECTRIC POWER SYSTEMS RESEARCH 2014年第May期110卷 144-153页

作者： Sanches, Danilo Sipoli London Junior, Joao Bosco A. Delbem, Alexandre Claudio B. Fed Technol Univ Parana BR-86300000 Cornell Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn BR-13566590 Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two multi-objective evolutionary algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II(NSGA-II) with NDE, named NSGA-N;(ii) the other is a multi-objective evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Large-scale distribution system Service restoration multiple faults Node-Depth Encoding multi-objective evolutionary algorithms

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

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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

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