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Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT)

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron I-56122 Pisa Italy

ISBN: (纸本)9789899507968

In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the rule base, and, for each variable, the number of fuzzy sets and the parameters of a piecewise linear transformation of the membership functions. We show the encouraging results obtained on a real world regression problem.

关键词： Accuracy-Interpretability Trade-off Granularity Learning Mamdani Fuzzy-Rule-Based Systems multi-objective evolutionary algorithms Piecewise Linear Transformation

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Minimizing total weighted tardiness and flow time for unrelated parallel machines scheduling with matching-improvement evolutionary algorithms

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IFAC Proceedings Volumes 2009年第4期42卷 1043-1048页

作者： Chiuh-Cheng Chyu Wei-Shung Chang Department of Industrial Engineering and Management Yuan-Ze University Taiwan

This paper addresses unrelated parallel machine scheduling problems with two minimization objectives: total weighted flow time and tardiness, and presents two hybrid methods based on (1) non-dominated sorting genetic algorithms (NSGA-II) and (2) strength Pareto evolutionary algorithm (SPEA). These algorithms were implemented in a different manner according to the following two features: (1) using random or fixed weighted sum direction search (RWSD or FWSD); (2) including or not including a bipartite weighted matching problem (BWMP). The performance of the algorithms is evaluated via two benchmark instances generated by a method in the literature. The experimental results indicate that algorithms with RWSD are superior to those with FWSD, and those including BWMP outperforms those not, in terms of proximity and spread metrics. In particular, NSGA-II with RWSD and BWMP performs best for the large size instance, whereas SPEA with RWSD and BWMP excels other algorithms in solving the medium size instance. Nevertheless, algorithms without BWMP spend much less computation time than others under the same termination criterion

关键词： multi-objective evolutionary algorithms unrelated parallel machines scheduling weighted flow time weighted tardiness weighted bipartite matching algorithm

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A COMPOSITIONAL METHOD USING AN evolutionary ALGORITHM FOR FINDING FUZZY RULES IN 3-LAYERED HIERARCHICAL FUZZY STRUCTURE

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2009年第4期8卷 467-467页

作者： Zajaczkowski, J. Verma, B. Queensland Climate Change Ctr Excellence Rockhampton Qld 4702 Australia CQ Univ Sch Comp Sci Rockhampton Qld 4702 Australia

This paper presents a novel compositional method for finding fuzzy rules in a three-layered hierarchical fuzzy structure. The proposed method incorporates a multi-objective evolutionary algorithm and a large set of initial conditions, including dynamical conditions of the system under investigation. The proposed method is focused on handling the large set of initial conditions by a multi-objective evolutionary algorithm and it can be applied to a wide range of dynamical control systems in robotics. The method has been evaluated on a dynamical system such as the inverted pendulum. The experimental results and analysis showed that the proposed method is much better than the existing methods such as amalgamation and single objective evolutionary algorithm based methods.

关键词： multi-objective evolutionary algorithms compositional methods hierarchical fuzzy control inverted pendulum

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Solving the ocean color inverse problem by using evolutionary multi-objective optimization of neuro-fuzzy systems

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2008年第5-6期12卷 339-355页

作者： Cococcioni, Marco Corsini, Giovanni Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron Informat Telecomunicaz Via Diotisalvi 2 I-56122 Pisa Italy

The ocean color inverse problem consists of determining the concentrations of optically active constituents, such as chlorophyll, suspended particulate matter and colored dissolved organic matter, from remotely sensed multispectral measurements of the reflected sunlight back-scattered by the water body. In this paper, we approach this regression problem by using an evolutionary multi-objective algorithm, namely the (2+2) Modified Pareto Archived evolutionary Strategy ((2+2)M-PAES), to optimize Takagi-Sugeno type (TS-type) fuzzy rule-based systems (FRBSs). Accuracy and complexity are the two competitive objectives to be simultaneously optimized. TS-type FRBSs are implemented as an artificial neural network;by training the neural network, the parameters of the fuzzy model are adjusted. In this way, the evolutionary optimization coarsely identifies the structure of the TS-type FRBSs, while the corresponding neural networks finely tune their parameters. As a result, a set of TS-type FRBSs with different trade-offs between accuracy and complexity is provided at the end of the optimization process. We show the effectiveness of our approach by comparing our results with those obtained on the ocean color inverse problem by other techniques recently proposed in the literature.

关键词： Ocean color Medium Resolution Imaging Spectrometer (MERIS) multi-objective evolutionary algorithms fuzzy rule-based systems Takagi-Sugeno fuzzy models Adaptive-Network-Based Fuzzy Inference System (ANFIS) (2+2) Modified-Pareto Archived evolutionary Strategy ((2+2)M-PAES)

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multi-objective design of PV-wind-diesel-hydrogen-battery systems

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RENEWABLE ENERGY 2008年第12期33卷 2559-2572页

作者： Dufo-Lopez, Rodolfo Bernal-Agustin, Jose L. Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain

This paper presents, for the first time, a triple multi-objective design of isolated hybrid systems minimizing, simultaneously, the total cost throughout the useful life of the installation, pollutant emissions CO2) and unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combination of components of the hybrid system and control strategies. As an example of application, a complex PV-wind-diesel-hydrogen-battery system has been designed, obtaining a set of possible solutions (Pareto Set). The results achieved demonstrate the practical utility of the developed design method. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： hybrid systems multi-objective design multi-objective evolutionary algorithms genetic algorithms

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Energy System Diagnosis by a Fuzzy Expert System with Genetically Evolved Rules

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INTERNATIONAL JOURNAL OF THERMODYNAMICS 2008年第3期11卷 115-121页

作者： Toffolo, Andrea Lazzaretto, Andrea Univ Padua Dept Mech Engn Via Venezia 1 I-35131 Padua Italy

The problem of malfunction diagnosis in energy systems can be approached using an expert system which compares the experimental data measured by the plant acquisition system and the calculated data evaluated by a plant simulator under the same operating conditions. In this paper the rules that form the "knowledge base" of the expert system are not assigned heuristically by trying to code the expertise of plant personnel, as it is usually done, but they are artificially and randomly generated by the recombination and selection operators of an evolutionary algorithm. A two-objective optimization problem is set up, in order to search for the optimal sets of rules having the minimum complexity but simultaneously maximizing the number of correct fault identifications for a given set of malfunctioning operating conditions. A global and a local approach are applied to a real test case, a two-shaft gas turbine used as the gas section of a combined-cycle cogeneration plant, in order to evaluate the potentialities and the limits of this methodology.

关键词： Diagnosis fuzzy expert system multi-objective evolutionary algorithms

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Hyper-spherical inversion transformations in multi-objective evolutionary optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期177卷 1678-1702页

作者： Large, J. W. Jones, D. F. Tamiz, M. Univ Portsmouth Dept Math Portsmouth PO1 3HE Hants England

multi-objective evolutionary algorithms (MOEAs) are widely considered to have two goals: convergence towards the true Pareto front and maintaining a diverse set of solutions. The primary concern here is with the first goal of convergence, in particular when one or more variables must converge to a constant value. Using a number of well known test problems, the difficulties that are currently impeding convergence are discussed and then a new method is proposed that transforms the decision space using the geometric properties of hyper-spherical inversions to converge towards/onto the true Pareto front. Future extensions of this work and its application to multi-objective optimisation is discussed. (c) 2005 Elsevier B.V. All rights reserved.

关键词： inversion transformation crossover convergence multi-objective evolutionary algorithms

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A multiobjective evolutionary approach for linearly constrained project selection under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期179卷 869-894页

作者： Medaglia, Andres L. Graves, Samuel B. Ringuest, Jeffrey L. Univ Los Andes Dept Ingn Ind Bogota Colombia Univ Los Andes Ctr Optimizac & Probabilida Aplicada Bogota Colombia Boston Coll Operat Informat & Strateg Management Dept Chestnut Hill MA 02167 USA

In the project selection problem a decision maker is required to allocate limited resources among an available set of competing projects. These projects could arise, although not exclusively, in an R&D, information technology or capital budgeting context. We propose an evolutionary method for project selection problems with partially funded projects, multiple (stochastic) objectives, project interdependencies (in the objectives), and a linear structure for resource constraints. The method is based on posterior articulation of preferences and is able to approximate the efficient frontier composed of stochastically nondominated solutions. We compared the method with the stochastic parameter space investigation method (PSI) and illustrate it by means of an R&D portfolio problem under uncertainty based on Monte Carlo simulation. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization project selection multi-objective evolutionary algorithms

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MRMOGA: a new parallel multi-objective evolutionary algorithm based on the use of multiple resolutions

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2007年第4期19卷 397-441页

作者： Lopez Jaimes, Antonio Coello Coello, Carlos A. CINVESTAV IPN Evolutionary Computat Grp EVOCINVDept Intn ElectSecc Computac Mexico City 07360 DF Mexico

In this paper, we introduce MRMOGA (multiple Resolution multi-objective Genetic Algorithm), a new parallel multi-objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well-defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state-of-the-art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi-objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright (c) 2006 John Wiley & Soris, Ltd.

关键词： parallel processing multi-objective evolutionary algorithms parallel multi-objective evolutionary algorithms multi-objective optimization evolutionary algorithms

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multi-objective analysis of a component-based representation within an interactive evolutionary design system

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ENGINEERING OPTIMIZATION 2007年第5期39卷 591-613页

作者： Machwe, A. T. Parmee, I. C. Univ W England ACDDM Grp Fac Comp Engn & Math Sci Bristol BS16 1QY Avon England

This article describes research relating to a user-centered evolutionary design system that evaluates both engineering and aesthetic aspects of design solutions during early-stage conceptual design. The experimental system comprises several components relating to user interaction, problem representation, evolutionary search and exploration and online learning. The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user. Additionally, the manner in which the user-interaction aspect affects system output is assessed by comparing Pareto frontiers generated with and without user interaction via a multi-objective evolutionary algorithm (MOEA). A solution clustering component is also introduced and it is shown how this can improve the level of support to the designer when dealing with a complex design problem involving multiple objectives. Supporting results are from the application of the system to the design of urban furniture which, in this case, largely relates to seating design.

关键词： multi-objective evolutionary algorithms interactive evolutionary design systems representation clustering

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