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IEEE SSD International multi-Conference on Systems, Signals and Devices

作者： Abdelghani Dendouga Slimane Oussalah Division Microélectronique et Nanotechnologie Centre de Développement des Technologies Avancées Algiers Algeria

ISBN: (纸本)9781479917594

In this paper, we elaborate a program based on multi-objective genetic algorithms (MOGAs) to allow automated optimization of analog circuits. The proposed methodology is used to find the optimal transistors sizes (length and width) in order to obtain operational amplifier performances for analog and mixed CMOS-based circuit applications. Eight performances are considered in this study, direct current (DC) gain, unity-gain bandwidth (GBW), phase margin (PM), power consumption (P), area (A), slew rate (SR), thermal noise and signal to noise ratio (SNR). The program is solved using MATLAB Optimization Toolbox™ solvers. Also by using variables obtained from genetic algorithms, the operational transconductance amplifier (OTA) is simulated by using Cadence Virtuoso Spectre circuit simulator in standard TSMC (Taiwan Semiconductor Manufacturing Company) RF 0.18μm CMOS technology. A good agreement is observed between the program optimization and electric simulation.

关键词： CMOS analog circuit design multi-objective genetic algorithms optimization two stage OTA

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NSGA-DO: Non-Dominated Sorting genetic Algorithm Distance Oriented

NSGA-DO: Non-Dominated Sorting Genetic Algorithm Distance Or...

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IEEE International Conference on Fuzzy Systems

作者： Adinovam H. M. Pimenta Heloisa de Arruda Camargo Department of Computer Science Federal University of Sao Carlos

ISBN: (纸本)9781467374293

In this work, a multi-objective genetic algorithm named Non-dominated Sorting genetic Algorithm Distance Oriented (NSGA-DO) is proposed. It has been designed as a modification of the well known NSGA-II. The proposed algorithm is able to find non-dominated solutions that balance the Pareto front with respect to optimization of the objectives. The main characteristic of NSGA-DO is the distance oriented selection of solutions. At each iteration, the non-dominated solutions are used to find an approximation to the Pareto front. The algorithm uses the locations of the solutions in the approximated frontier to find the best distribution of solutions, which will guide the selection operations. In order to validate the proposal, NSGA-DO was applied in the context of multi-objective Evolutionary Fuzzy Systems (MOEFS), to the generation of fuzzy knowledge bases for classification. The study focus on the evaluation of the distribution of non-dominated solution as well as on the accuracy-interpretability trade-off. Experiments show the superiority of NSGA-DO when compared to NSGA-II in all three issues analyzed: dispersion of non-dominated solutions, accuracy and interpretability of the generated systems.

关键词： multi-objective genetic algorithms multi-objective Evolutionary Fuzzy Systems Automatic Generation of Fuzzy Rule Pareto-Optimal Front

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Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms

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EVOLUTIONARY INTELLIGENCE 2009年第1-2期2卷 39-51页

作者： Marquez, Antonio A. Marquez, Francisco A. Peregrin, Antonio Univ Huelva Informat Technol Dept Huelva Spain

In this paper, we present an evolutionary multi-objective learning model achieving cooperation between the rule base and the adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference adaptive operators together with rules. The multi-objective evolutionary algorithm proposed generates a set of fuzzy rule based systems with different trade-offs between interpretability and accuracy, allowing the designers to select the one that involves the most suitable balance for the desired application. We develop an experimental study testing our approach with some variants on nine real-world regression datasets finding the advantages of cooperative compared to sequential models, as well as multi-objective compared with single-objective models. The study is elaborated comparing different approaches by applying non-parametric statistical tests for pair-wise. Results confirm the usefulness of the proposed approach.

关键词： Linguistic fuzzy modelling Interpretability-accuracy trade-off multi-objective genetic algorithms Adaptive inference system Adaptive defuzzification Rule learning

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METSK-HD^e: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems

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INFORMATION SCIENCES 2014年 276卷 63-79页

作者： Gacto, M. J. Galende, M. Alcala, R. Herrera, F. Univ Jaen Dept Comp Sci Jaen 23071 Spain CARTIF Ctr Tecnol Valladolid 47151 Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol North Jeddah Jeddah 21589 Saudi Arabia

In this contribution, we propose a two-stage method for Accurate Fuzzy Modeling in High-Dimensional Regression Problems using Approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, an evolutionary data base learning is performed (involving variables, granularities and slight fuzzy partition displacements) together with an inductive rule base learning within the same process. The second stage is a post-processing process to perform a rule selection and a scatter-based tuning of the membership functions for further refinement of the learned solutions. Moreover, the second stage incorporates an efficient Kalman filter to learn the coefficients of the consequent polynomial function in the Takagi-Sugeno-Kang rules. Both stages include mechanisms that significantly improve the accuracy of the model and ensure a fast convergence in high-dimensional and large-scale regression datasets. We tested our approach on 28 real-world datasets with different numbers of variables and instances. Five well-known methods have been executed as references. We compared the different approaches by applying non-parametric statistical tests for pair-wise and multiple comparisons. The results confirm the effectiveness of the proposed method, showing better results in accuracy within a reasonable computing time. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Accurate fuzzy modeling Takagi-Sugeno-Kang rules multi-objective genetic algorithms Embedded genetic data base learning Regression High-dimensional and large-scale problems

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An exploratory approach to spatial decision support

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2014年第May期45卷 101-113页

作者： Jankowski, Piotr Fraley, Grant Pebesma, Edzer San Diego State Univ Dept Geog San Diego CA 92182 USA Adam Mickiewicz Univ Inst Geoecol & Geoinformat Poznan Poland Univ Munster Inst Geoinformat Munster Germany

The paper presents a decision support approach to solving problems characterized by spatially-explicit decision variables, multiple objectives, and preferences for ancillary decision criteria. The approach offers a three-step workflow, in which Pareto non-dominated solutions to a multi-objective decision problem are generated with a spatially adaptive genetic algorithm, objective value trade-offs are examined in an interactive graphics environment, and the selected solution alternatives are evaluated on the bases of ancillary multiple criteria. The three-step workflow is demonstrated on the example of a selection problem involving alternative configurations of sensors for radioactivity monitoring in a trans-border region including the state of Lower Saxony in Germany and the Netherlands. The presented approach promotes the search for diverse, non-dominated solution alternatives by coupling a fuzzy logic system with spatially adaptive genetic operators. The three-step workflow offers a comprehensive approach to spatial decision support starting with diverse option generation, through exploration of decision objective trade-offs, to multiple criteria evaluation of the selected non-dominated decision alternatives. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Spatial decision support systems Spatial optimization multi-objective genetic algorithms multiple criteria decision analysis

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Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2012年第12期25卷 1139-1150页

作者： Bevilacqua, Vitoantonio Costantino, Nicola Dotoli, Mariagrazia Falagario, Marco Sciancalepore, Fabio Politecn Bari Dipartimento Elettrotecn & Elettron I-70125 Bari Italy Politecn Bari Dipartimento Ingn Meccan & Gest I-70126 Bari Italy

The paper addresses the optimal design of distribution networks (DNs). Considering a distributed system composed of stages connected by material links labelled with suitable performance indices, a procedure employing multi-objective genetic algorithms (MOGAs) is presented to select the optimal DN configuration. The paper enhances a deterministic procedure for DN strategic configuration by employing MOGACOP, a real-valued chromosome MOGA that can be applied to the case of constrained nonlinear function. The main MOGA characteristics are the presence of three populations: two reference sets of individuals satisfying all constraints, namely, a set of Pareto optimal individuals (frontier population) and a set of individuals covering the previous population (archive population), together with a search set which, on the contrary, includes individuals that are allowed to not satisfy all constraints (laboratory population). MOGACOP allows solving the DN design nonlinear problem, which exhibits a multi-objective function that varies linearly only with some variables and nonlinearly with the remaining variables. The proposed MOGA application allows finding a Pareto frontier of optimal solutions, which is compared with the frontier obtained by solving the same problem with Integer Linear Programming (ILP), where piecewise constant contributions are linearly approximated. The two found curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimisation and the ILP models are applied under structural constraints to a case study describing the distribution chain of a large enterprise of southern Italy producing consumer goods.

关键词： supply chain distribution network optimisation integer linear programming multi-objective genetic algorithms

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Optimizing reliability-based robust design model using multi-objective genetic algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第2期66卷 301-310页

作者： Rathod, Vijay Yadav, Om Prakash Rathore, Ajay Jain, Rakesh Malaviya Natl Inst Technol Dept Mech Engn Jaipur 302017 Rajasthan India N Dakota State Univ Dept Ind & Mfg Engn Fargo ND 58105 USA

Reliability-based robust design optimization (RBRDO) is one of the most important tools developed in recent years to improve both quality and reliability of the products at an early design stage. This paper presents a comparative study of different formulation approaches of RBRDO models and their performances. The paper also proposes an evolutionary multi-objective genetic algorithm (MOGA) to one of the promising hybrid quality loss functions (HQLF)-based RBRDO model. The enhanced effectiveness of the HQLF-based RBRDO model is demonstrated by optimizing suitable examples. Published by Elsevier Ltd.

关键词： Reliability-based design optimization Robust design optimization multi-objective genetic algorithms

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multi-objective genetic learning of serial hierarchical fuzzy systems for large-scale problems

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SOFT COMPUTING 2013年第1期17卷 165-194页

作者： Benitez, Alicia D. Casillas, Jorge Univ Granada Dept Comp Sci & Artificial Intelligence CITIC UGR Res Ctr Informat & Commun Technol E-18071 Granada Spain CSIC Calculat Ctr Astrophys Inst Andalusia IAA Granada 18008 Spain

When we face a problem with a high number of variables using a standard fuzzy system, the number of rules increases exponentially and the obtained fuzzy system is scarcely interpretable. This problem can be handled by arranging the inputs in hierarchical ways. This paper presents a multi-objective genetic algorithm that learns serial hierarchical fuzzy systems with the aim of coping with the curse of dimensionality. By means of an experimental study, we have observed that our algorithm obtains good results in interpretability and accuracy with problems in which the number of variables is relatively high.

关键词： Curse of dimensionality Hierarchical fuzzy systems multi-objective genetic algorithms Variable selection

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ADAPTIVE ZONING DESIGN BY SUPERVISED LEARNING USING multi-objective OPTIMIZATION

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2014年第1期13卷 1450004-1450004页

作者： Mangini, F. Pirlo, G. Univ Bari Dept Comp Sci Via E Orabona 4 Bari Italy

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

关键词： Zoning method supervised learning handwritten digit recognition multi-objective genetic algorithms Voronoi diagrams

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NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第4期40卷 1205-1212页

作者： Ak, Ronay Li, Yanfu Vitelli, Valeria Zio, Enrico Droguett, Enrique Lopez Couto Jacinto, Carlos Magno Ecole Cent Paris Chair Syst Sci & Energet Challenge European Fdn New Energy Elect France F-92290 Chatenay Malabry France SUPELEC F-91192 Gif Sur Yvette France Politecn Milan Dept Energy I-20133 Milan Italy Univ Fed Pernambuco Ctr Risk Anal & Environm Modeling Recife PE Brazil CENPES Petrobras Res Ctr Rio De Janeiro Brazil

Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (Pis) of the scale deposition rate. The Pls are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing Pls with both high coverage and small width. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Prediction intervals Neural networks multi-objective genetic algorithms Cross-validation Hypervolume Scale deposition rate

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