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IEEE International Conference on System, Man, and Cybernetic

作者： Zhang, Wenqiang Gen, Mitsuo Lin, Lin Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781424423835

The Assembly Line Balancing (ALB) problem is a well-known manufacturing optimization problem, which determines the assignment of various tasks to an ordered sequence of stations, while optimizing one or more objectives without violating restrictions Imposed on the line. As genetic algorithms (GAs) have established themselves as a useful optimization technique in the manufacturing field, the application of GAs to ALB problem has expanded a lot. This paper describes a generalized Pareto-based scale-independent fitness function (gp-siffGA) for solving ALB problem with worker allocation (ALB-wa) to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. For this appraoch, first a random key-based representation method adapting the GA was proposed. Following, advanced genetic operators adapted to the specific chromosome structure and the characteristics of the ALB-wa problem were used. Moreover, Pareto dominance relationship was used to solve the ALB-wa problem without using relative preferences of multiple objectives. Finally, the performance of proposed method was validated through numerical experiments. The results indicated that the proposed approach improved the quality of solutions more than the other existing GA approaches.

关键词： Assembly line balancing generalized Pareto-based scale-independent fitness function multiobjective genetic algorithm random key

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multiobjective genetic Fuzzy PID Control Design for Uncertain Dynamic Systems

Multiobjective Genetic Fuzzy PID Control Design for Uncertai...

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13th Biennial Ibero-American Conference on Artificial Intelligence (AI)

作者： Pires, Danubia Soares de Oliveira Serra, Ginalber Luiz Fed Inst Educ Sci & Technol Dept Electroelect Lab Computat Intelligence Appl Technol BR-65030005 Sao Luis Maranhao Brazil

ISBN: (纸本)9783642346538;9783642346545

In this paper, a robust fuzzy digital PID control strategy, via multiobjective genetic algorithm, based on the gain and phase margins specifications, with applications to uncertain dynamic systems with time delay, is proposed. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. Computational results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the fuzzy PID control system compared to the specified ones and tracking of the reference trajectory compared to Ziegler-Nichols method.

关键词： robust control fuzzy system digital PID controller uncertain dynamic system multiobjective genetic algorithm

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AMI Screening Using Linguistic Fuzzy Rules

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JOURNAL OF MEDICAL SYSTEMS 2012年第2期36卷 463-473页

作者： Ainon, Raja Noor Bulgiba, Awang M. Lahsasna, Adel Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Univ Malaya Julius Ctr Fac Med CRYSTALFac Sci Kuala Lumpur 50603 Malaysia

This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.

关键词： AMI Fuzzy rules Prediction system multiobjective genetic algorithm

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A New Methodology for Model Based Robust Fuzzy Digital PID Controller Design

A New Methodology for Model Based Robust Fuzzy Digital PID C...

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3rd Global Congress on Intelligent Systems (GCIS)

作者： Pires, Danubia Soares de Oliveira Serra, Ginalber Luiz Fed Inst Educ Sci & Technol Dept Electroelect Lab Computat Intelligence Appl Technol Sao Luis MA Brazil

ISBN: (纸本)9781467330725;9780769548609

In this paper, a robust fuzzy digital PID control strategy, via multiobjective genetic algorithm, based on the gain and phase margins specifications, with applications to uncertain dynamic systems with time delay, is proposed. A mathematical formulation based on the gain and phase margins, the fuzzy model and PID digital controller structures, and the time delay of the uncertain dinamic system, is deduced. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. Computational results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the fuzzy PID control system compared to the specified ones and tracking of the reference trajectory.

关键词： robust control fuzzy systems digital PID controller uncertain dynamic systems multiobjective genetic algorithm

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Survivable and Impairment-Aware Virtual Topologies for Reconfigurable Optical Networks: a Cognitive Approach

Survivable and Impairment-Aware Virtual Topologies for Recon...

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4th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT)

作者： Fernandez, N. Duran, R. J. de Miguel, I. Merayo, N. Aguado, J. C. Fernandez, P. Jimenez, T. Rodriguez, I. Sanchez, D. Lorenzo, R. M. Abril, E. J. Angelou, M. Tomkos, I. Univ Valladolid ETSI Telecomunicac Paseo Belen 15 E-47011 Valladolid Spain Athens Informat Technol Ctr Athens 19002 Greece

ISBN: (纸本)9781467320153;9781467320160

In this paper, we present a new version of a multiobjective genetic algorithm to design virtual topologies with the aim of reducing both the energy consumption and the network congestion. Moreover, we also propose an improved version of that algorithm, by including two cognitive techniques. Unlike the original version, the new proposals design survivable virtual topologies, as they provide shared path protection thus offering fault-tolerance capability with efficient resource utilization. Moreover, the proposed methods also take into account physical impairments in order to ensure that the survivable virtual topologies fulfill Quality of Transmission requirements in any scenario without failures or with a single cable failure. Finally, a simulation study demonstrates the advantages of the use of cognition.

关键词： Cognition failure protection multiobjective genetic algorithm power consumption QoT requirements virtual topologies

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Multicriteria Fuzzy Clustering for Brain Image Segmentation

Multicriteria Fuzzy Clustering for Brain Image Segmentation

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International Conference on Modeling, Simulation and Applied Optimization

作者： Olfa Limam Fouad Ben Abdelaziz LARODEC Laboratory Institut Supérieur de Gestion University of Tunis Bardo Tunisia Rouen Business School Rouen France

ISBN: (纸本)9781467358125

One of the most challenging task in image analysis is to identify correctly tissues where boundaries are generally not clear. Fuzzy clustering is supposed to be the most appropriate to model this situation in applications such as tissue classification, tumor detection. While, image segmentation using fuzzy clustering technique classifies correctly pixels of an image with a great extent of accuracy [1], recent works have shown that fuzzy clustering techniques considers a single objective may not provide a good result since no single validity measure works well on different kinds of data sets. Moreover, a wrong choice of a validity measure leads to poor results [2]. In this paper, we introduce a multiobjective fuzzy clustering approach producing a set of Pareto solutions among which the best solution, based on I-index validation measure, is chosen to be the final clustering solution. First, a spatial information is considered to deal more effectively with the noise and intensity inhomogeneities introduced in imaging process. Second, we propose to use a variable string length encoding technique to automatically identify the number of clusters, given that it does not require a prior knowledge about number of clusters present in a data set. Therefore, an initializing method based on a center approximation approach is proposed to accelerate the clustering process and make results more robust. Applied to normal and multiple sclerosis lesion magnetic resonance image brain images, our method shows better performance than competing algorithms.

关键词： Fuzzy c-means clustering multiobjective genetic algorithm MRI brain image

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Feedback-Based Global Instruction Scheduling for GPGPU Applications 1

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12th International Conference on Computational Science and Its Applications (ICCSA)

作者： Timm, Constantin Goerlich, Markus Weichert, Frank Marwedel, Peter Mueller, Heinrich TU Dortmund Dortmund Germany

ISBN: (数字)9783642311253

ISBN: (纸本)9783642311246;9783642311253

In the face of the memory wall even in high bandwidth systems such as GPUs, an efficient handling of memory accesses and memory-related instructions is mandatory. Up to now, memory performance considerations were only made for GPGPU applications at source code level. This is not enough when optimizing an application towards high performance: The code has to be optimized at assembly level as well. Due to the spreading of GPGPU-capable hardware in smaller and smaller devices, the energy consumption of a program is - besides the performance - an important optimization goal. In this paper, a novel compiler optimization technique, called FALIS (Feedback-based and memory-Aware gLobal Instruction Scheduling), is presented based on global instruction scheduling and multi-objective genetic algorithms. The approach uses a profiling-based feedback in order to take the measured performance and energy consumption values inside a compiler into account. Profiling on the real hardware platform is important in order to consider the characteristics of the underlying hardware. FALIS increases runtime performance of a GPGPU application by up to 13.02% and decreases energy consumption by up to 10.23%.

关键词： Energy-Aware Systems Compilers GPGPU multiobjective genetic algorithm Profiling

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Distributed Generation and Network Upgrades Placements for Distribution System Expansion Planning

Distributed Generation and Network Upgrades Placements for D...

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Su, Chun-Lien Natl Kaohsiung Marine Univ Dept Marine Engn Kaohsiung Taiwan

ISBN: (纸本)9783037853191

In contrast with the traditional distribution expansion planning approach, significant giants can be realized in terms of outage loss, supply quality, and load carrying capacity from the distribution expansion with a suitable size and siting of distributed generation (DG). However, in order to achieve these planning goals, it often requires network upgrades to accommodate the anticipated power flows in the distribution system with DG. To improve utilization and expansion of distribution system, this paper proposes a methodology to derive the optimal strategy of generation expansion and network upgrades including line capacity upgrade, line switches, and network reconfiguration for multiobjective distribution expansion planning by minimizing network investment cost, feeder losses, customer interruption cost, and voltage drop. A multiobjective genetic algorithm (MOGA) is employed to quantify the effects of DG and network upgrades placements on distribution network under load increases and during distribution outages. To demonstrate the effectiveness of the proposed methodology, an IEEE feeder test system is selected for computer simulation to explore the benefits of DG placement for distribution system utilization and expansion.

关键词： Distributed energy resources System planning multiobjective genetic algorithm

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A multiobjective Go based Approach to Protein Complex Detection

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Procedia Technology 2012年 4卷 555-560页

作者： Sumanta Ray Moumita De Anirban Mukhopadhyay Department of Computer Science and Engineering University of Kalyani Kalyani-741235 West Bengal India

Considering the large number of proteins and the high complexity of the protein interaction network, decomposing it into smaller and manageable modules is a valuable step toward the analysis of biological processes and pathways. There are di_erent types of graph clustering approaches available for modularization of PPI networks. But most of them seek to identify groups or clusters of proteins on the basis of the local density of the interaction graph in the network. So they find the denser region in the interaction graph, as the denser regions are expected to have the high chance to form a protein complex. In this article we present a novel multiobjective Gene Ontology based Clustering (MGOC) in PPI network- to find protein complexes. We consider both graphical properties of PPI network as well as biological properties based on GO semantic similarity as objective functions. The proposed technique is demonstrated on yeast PPI data and the results are compared with that of some existing algorithms.

关键词： Protein-protein interaction protein complex multiobjective genetic algorithm gene ontology

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Survivable and Impairment-Aware Virtual Topologies for Reconfigurable Optical Networks: a Cognitive Approach

Survivable and Impairment-Aware Virtual Topologies for Recon...

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International Congress on Ultra Modern Telecommunications and Control Systems

作者： N. Fernandez R. J. Duran I. de Miguel N. Merayo J. C. Aguado P. Fernandez T. Jiménez I. Rodriguez D. Sanchez R. M. Lorenzo E. J. Abril M. Angelou I. Tomkos University of Valladolid ETSI de Telecomunicación Paseo de Belen 15 47011 Valladolid Spain Athens Information Technology Center 19. 5 km Markopoulou Ave. 19002 Peania Athens Greece

ISBN: (纸本)9781467320160

关键词： Cognition failure protection multiobjective genetic algorithm power consumption QoT requirements virtual topologies

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