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2010 IEEE World Congress on Computational Intelligence

作者： Masafumi, Yamamoto Tomohiro, Yoshikawa Takeshi, Furuhashi Nagoya Univ Dept Comp Sci & Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781424481262

genetic algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep a diversity of solutions, to Multi-Objective genetic algorithm (MOGA) have been conducted. In MOGA, it is difficult to find the solutions which satisfy all objective functions because of their trade-off. Especially when there are many objective functions, it is obvious that it needs a lot of time to search for effective Pareto solutions and find them. This paper proposes the interactive way of addition and deletion of islands to the original ones based on user's requirements with the visualization of acquired solutions in island model for MOGA. This paper applies the proposed method to Nurse Scheduling Problem (NSP) using the visualization by Principal Component Analysis (PCA). Through the experiment, it is confirmed that an interactive tuning of the weights for the objective functions leaded to the acquisition of better Pareto solutions which a user wants while they are difficult to be acquired by the prepared weights.

关键词： MOGA algorithm Pareto optimisation Pareto solutions data visualisation genetic algorithms interactive island model multiobjective genetic algorithm nurse scheduling problem principal component analysis principal component analysis scheduling visualization

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Multi-objective Optimal Reservoir Operation

Multi-objective Optimal Reservoir Operation

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2010 IEEE World Congress on Computational Intelligence

作者： Scola, Luis A. Neto, Oriane M. Takahashi, Ricardo H. C. Cerqueira, Sergio A. A. G. Univ Fed Sao Joao del Rei Dept Thermal & Fluid Sci Praca Frei Orlando 170 BR-36307352 Sao Joao Del Rei Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Mat BR-31270 Belo Horizonte MG Brazil Fed Univ Sao Joao Dept Mech Engn BR-36307 Orlando Brazil

ISBN: (纸本)9781424481262

The need for the efficient operation of hidropower plants, which provides most of the electrical power consumed in Brazil, is related not only to the issue of energy conservation, but has also been highlighted by the increasing opposition to the construction of new large reservoirs, for ecological and social reasons. In this work, a multi-objective genetic algorithm is applied to problem of the optimization of a single Brazilian hydropower plant, with the objectives of increasing the net energy generation along the year and reducing the peak of demand of non-renewable energy sources. To increase the performance of the algorithm, two new formulations for the problem are proposed, with different ways of dealing with the operational constraints. In comparison with the more traditional approach, this results not only in efficiency gains, but also in an expanded Pareto front, which adds more flexibility to the system, by revealing new possible configurations of system operation.

关键词： Brazilian hydropower plant Pareto front Pareto optimisation electrical power energy conservation genetic algorithms hydroelectric power stations multiobjective genetic algorithm multiobjective optimal reservoir operation net energy generation nonrenewable energy sources reservoirs

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A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference

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PATTERN RECOGNITION 2005年第9期38卷 1444-1456页

作者： Delgado, M Pegalajar, MC Univ Granada Dept Ciencias Computac & Intelegencia Artificial ETSi Informat E-18071 Granada Spain

Grammatical inference has been extensively studied in recent years as a result of its wide field of application, and in turn, recurrent neural networks have proved themselves to be a good tool for grammatical inference. The learning algorithms for these neural networks, however, have been far less studied than those for feed-forward neural networks. Classical training methods for recurrent neural networks suffer from being trapped in local minimal and having a high computational time. In addition, selecting the optimal size of a neural network for a particular application is a difficult task. This suggests that the problems of developing methods to determine optimal topologies and new training algorithms should be studied. In this paper, we present a multi-objective evolutionary algorithm which is able to determine the optimal size of recurrent neural networks in any particular application. This is specially analyzed in the case of grammatical inference: in particular, we study how to establish the optimal size of a recurrent neural network in order to learn positive and negative examples in a certain language, and how to determine the corresponding automaton using a self-organizing map once the training has been completed. (c) 2005 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

关键词： recurrent neural networks multiobjective genetic algorithm grammatical inference

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multiobjective evolutionary approach to cost-effective traffic grooming in unidirectional SONET/WDM rings

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PHOTONIC NETWORK COMMUNICATIONS 2009年第1期18卷 105-115页

作者： Biswas, Utpal Maulik, Ujjwal Mukhopadhyay, Anirban Naskar, Mrinal Kanti Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

Traffic grooming in optical networks is the process of multiplexing and demultiplexing low-speed traffic streams onto high-speed wavelengths. The research in the domain of traffic grooming mainly focuses on minimizing number of SONET add/drop multiplexers (SADMs) in SONET/WDM rings and it has been shown that they can potentially be reduced by careful assignment of low-speed traffic streams onto high-speed wavelengths. However, the cost of the network not only depends on the number of SADMs, but also the number of wavelengths and the grooming ratio. It is often the case that all of them cannot be minimized simultaneously. In this article, the problem of minimization of cost of a SONET/WDM unidirectional ring has been modeled as a multiobjective optimization problem which simultaneously minimizes the number of SADMs, the number of wavelengths, and the grooming ratio. A popular multiobjective genetic algorithm (NSGA-II) has been used as the underlying optimization tool. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Performance of the proposed algorithm has been demonstrated on different network topologies.

关键词： Wavelength division multiplexing Traffic grooming SONET add/drop multiplexers Grooming ratio multiobjective genetic algorithm Pareto-optimality

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Development of a multiobjective GA for advanced planning and scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2009年第9-10期42卷 974-992页

作者： Dayou, Liu Pu, Yan Ji, Yu Jilin Univ Coll Comp Sci & Technol Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

In this paper, we consider an advanced planning and scheduling (APS) problem in manufacturing supply chain. The problem was formulated with mixed integer programming and three objectives are taken into account. To solve the APS model, a multiobjective genetic algorithm with local search is presented to find the Pareto optimal solutions. The proposed algorithm makes use of the principle of nondominated sorting, coupled with the use of a metric for normalized crowding distance. Local search technique is used to improve the efficiency. The proposed algorithm was compared with two other multiobjective genetic algorithms from the literature. Performance of these heuristics has been tested on ten problems in three scenarios. The computational results demonstrate the effectiveness and efficiency of the proposed approach and indicate that the presented algorithm outperforms previous work for APS problems.

关键词： Advanced planning multiobjective genetic algorithm genetic local search

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Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

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INFORMATION PROCESSING LETTERS 2009年第16期109卷 887-896页

作者： Garcia-Nieto, J. Alba, E. Jourdan, L. Talbi, E. Univ Malaga Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain LIFL INRIA Futurs F-59650 Villeneuve Dascq France

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity. both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines: in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way. a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis Of Our approach using two and three objectives, and with other existing algorithms. suggest that our proposal is highly appropriate for solving this problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： algorithms Analysis of algorithms Combinatorial problems Databases Design of algorithms Performance evaluation Sensitivity and specificity analysis multiobjective genetic algorithm Microarray gene selection

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multiobjective planning of open-loop MV distribution networks using ComGIS network analysis and MOGA

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第2期79卷 390-398页

作者： Kong, Tao Cheng, Haozhong Hu, Zechun Yao, Liangzhong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China AREVA T&D Technol Ctr Stafford ST17 4LX England

An advanced method for optimal planning of open-loop medium voltage (MV) distribution network is proposed in this paper. The multiobjective planning model of open-loop MV distribution network is formulated, and an outage cost calculating method applicable for open-loop MV distribution networks is proposed. To solve the optimization problem, a special multiobjective genetic algorithm (MOGA) is designed based on the network dataset built by Component Geographical Information Systems (ComGIS). The network analysis function of the ComGIS is embedded in the overall optimization process of the MOGA to find single-loop optimal paths. Crossover and mutation operator are designed according to characteristics of the coding. The evolutionary orientation is directed by the fitness function based on the Pareto order of individuals. The Pareto Optimal Set (POS) including several candidate planning schemes is obtained through the MOGA, from which the recommended scheme is selected. The practicability of the method is tested by its application to a real distribution system. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Medium-voltage distribution network Open-loop configuration multiobjective genetic algorithm Component Geographical Information Systems Distribution network planning

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Control of a benchmark structure using GA-optimized fuzzy logic control

Control of a benchmark structure using GA-optimized fuzzy lo...

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作者： Shook, David Adam Texas A&M University

Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation.

关键词： structural control fuzzy logic control magnetorheological dampers experimental testing neuro-fuzzy modeling system identification acceleration feedback control multiobjective genetic algorithm Book Thesis

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Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach

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FUNDAMENTA INFORMATICAE 2008年第4期86卷 411-428页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

A popular approach for landcover classification in remotely sensed satellite images is clustering the pixels in the spectral domain into several fuzzy partitions. It has been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. Motivated by this observation, in this article a two-stage fuzzy clustering algorithm is described that utilizes the concept of points having significant membership to multiple classes. The points situated in the overlapped regions of different clusters are first identified and excluded from consideration while clustering. Thereafter, these points are given class labels based on Support vector Machine classifier which is trained by the remaining points. The well known Fuzzy C-Means algorithm and some recently proposed genetic clustering schemes are utilized in the process. The effectiveness of the two-stage clustering technique has been demonstrated on IRS remote sensing satellite images of the cities of Bombay and Calcutta and compared with other well known clustering techniques. Also statistical significance test has been carried out to establish the statistical significance of the clustering results.

关键词： Unsupervised pixel classification significant multiclass membership cluster validity index variable string length genetic algorithm multiobjective genetic algorithm Support Vector Machine

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multiobjective Fuzzy Biclustering in Microarray Data: Method and a New Performance Measure

Multiobjective Fuzzy Biclustering in Microarray Data: Method...

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IEEE Congress on Evolutionary Computation

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Bandyopadhyay, Sanghamitra Zhang, Michael Q. Zhang, Xuegong Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Indian Stat Inst Machine Inst Unit Kolkata 700108 India Cold Spring Harbor Lab Huntington NY USA Tisnghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781424418220

Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms.

关键词： Fuzzy biclustering fuzzy K-medoids mean squared residue expression profile variance multiobjective genetic algorithm variable string length statistical difference from background

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