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检索条件"主题词=multiobjective genetic algorithm"

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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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Multi-performance optimization in wire EDM of Inconel 617 using GRA and genetic algorithm 2nd

Multi-performance optimization in wire EDM of Inconel 617 us...

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2nd International Conference on Functional Material, Manufacturing and Performances (ICFMMP)

作者： Nair, Anish Kumanan, S. Shanavas, K. P. Kalasalingam Acad Res & Educ Dept Mech Engn Krishnankoil India Natl Inst Technol Tiruchirappalli Dept Prod Engn Tiruchirappalli India Coll Engn Vadakara Dept Mech Engn Kozhikode Kerala India

Inconel 617 superalloy is a candidate material for various high temperature components employed in ultra-supercritical power plants. Superalloys are hard to machine materials that easier to process using nontraditional methods such as wire electrical discharge machining. This paper discusses multi perfor-mance optimization in machining of Inconel 617 using grey relational analysis and genetic algorithm. The performance characteristics of wire cut EDM are surface roughness, material removal rate, wire wear rate, form and orientation tolerances. Parameters under consideration are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. The performance measures are consolidated as three performance indices using the grey relational grade. The mathematical models to estimate the performance objectives are developed using regression analysis. multiobjective optimiza-tion is applied through genetic algorithm and a pareto front containing the optimized responses is com-puted. The optimized parameters are validated experimentally and the results are reported. Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Con-ference on Functional Material, Manufacturing and Performances

关键词： Wire electrical discharge machining Inconel 617 Ultra-supercritical power plants Grey relational analysis multiobjective genetic algorithm

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

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3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Kong Tao Cheng Haozhong Hu Zechun Wang Chun Huang Wei Chen Chunqin Gao Yubo Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9787564112493

In this paper, an advanced method for the planning of open-loop medium voltage (MV) distribution network is proposed. The multiobjective planning model is constructed, and 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 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 Pareto order of individuals. The Pareto optimal set (POS) including several candidate planning schemes is gotten through MOGA, from which the recommended scheme is selected. The method has been applied to real life power distribution networks, showing its potential in practical applications.

关键词： component geographical information systems distribution network planning medium-voltage distribution network multiobjective genetic algorithm network analysis open-loop configuration

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A new efficient and useful robust optimization approach - Design for multi-objective six sigma

A new efficient and useful robust optimization approach - De...

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IEEE Congress on Evolutionary Computation

作者： Shimoyama, K Oyama, A Fujii, K Univ Tokyo Dept Aeronaut & Astronaut Sagamihara Kanagawa 2298510 Japan

ISBN: (纸本)0780393635

An efficient and useful robust optimization approach, design for multi-objective six sigma (DFMOSS), has been developed. The DFMOSS couples the ideas of design for six sigma (DFSS) and multi-objective genetic algorithm (MOGA) to solve drawbacks of DFSS;DFMOSS obtains trade-off solutions between optimality and robustness in one optimization. In addition, it does not need careful parameter tuning. Robust optimizations of a test function and welded beam design problem demonstrated that DFMOSS is more effective and more useful than DFSS.

关键词： design engineering genetic algorithms six sigma (quality) welding design for multiobjective six sigma multiobjective genetic algorithm optimization test function welded beam design problem

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Hybrid Artificial Neural Network and genetic algorithm Model for Multi-Objective Strength Optimization of Concrete with Surkhi and Buntal Fiber 2020

Hybrid Artificial Neural Network and Genetic Algorithm Model...

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12th International Conference on Computer and Automation Engineering (ICCAE)

作者： Silva, Dante L. de Jesus, Kevin Lawrence M. Villaverde, Bernard S. Adina, Edgar M. Mapua Univ 658 Muralla St Manila 1002 Philippines

ISBN: (纸本)9781450376785

Fiber-reinforced concrete (FRC) is one of the efficient innovation in concrete industry that has the ability to enhance the mechanical properties significantly. To cope up with the increase in infrastructural activities which resulted in greater demand in production of different construction materials have a negative impact on the environment, this study aims to determine the mechanical performance of the optimum compressive and flexural strength of buntal fiber-reinforced concrete with surkhi as partial replacement for sand (BFRC-SS). Using 28th-day compressive and flexural strength, several mixtures were experimentally tested to derive a mix proportion that gave the best mechanical properties of BFRC-SS. From the results, best hybrid models of compressive and flexural strength were formulated using Artificial Neural Network (ANN). Results showed that ANN was able to establish the effects of surkhi and buntal (Corypha utan Lam) fiber to the mechanical properties of BFRC-SS. Furthermore, the multi-objective genetic algorithm (GA) model generated the optimum proportion for the best compressive and flexural strength. Fuzzy Inference System (FIS) and Multi-Linear Regression Analysis (MLRA) were also utilized to assess and validate the hybrid model through surface imaging Utilizing least percent error, ANN hybrid model showed the most significant predictive model compared to other models generated by MLRA and FIS. This study adoptied the fusion of 4.0 Industrial Revolution and favoring creativity and integrity through artificial intelligence.

关键词： Surkhi Buntal Fiber Artificial Neural Network multiobjective genetic algorithm Fuzzy-Inference System MultiLinear Regression Analysis

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Simultaneous Informative Gene Selection and Clustering through multiobjective Optimization

Simultaneous Informative Gene Selection and Clustering throu...

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2010 IEEE World Congress on Computational Intelligence

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 West Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 West Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

ISBN: (纸本)9781424481262

Clustering methods are used for unsupervised classification of tumor subclasses in microarray gene expression data sets organized in a fashion where the rows represent the tumor samples and columns represent the genes. Clustering algorithms can be very sensitive with respect to the set of features (genes) considered in the clustering process. It is important to select the set of informative and relevant genes to be used for clustering. In this article, a multiobjective genetic algorithm based technique has been proposed for performing the tasks of gene selection and fuzzy clustering simultaneously. A novel encoding technique is developed in this regard and the algorithm searches for the best cluster centers while minimizing the number of selected genes. The number of clusters is evolved automatically. The performance of the proposed technique has been illustrated on an artificial data set and compared with that of several other related feature selection/clustering approaches. Moreover its performance is demonstrated on two real life multi-class gene expression data sets viz., Brain tumor and Lung tumor data sets.

关键词： artificial data set bioinformatics encoding encoding technique fuzzy clustering genetic algorithms genomics informative gene clustering informative gene selection medical computing microarray gene expression multiobjective genetic algorithm multiobjective optimization pattern classification pattern clustering tumor subclass classification tumours unsupervised classification

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Evolutionary multiobjective optimization with a segment-based external memory support for the multiobjective quadratic assignment problem

Evolutionary multiobjective optimization with a segment-base...

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IEEE Congress on Evolutionary Computation

作者： Acan, A Ünveren, A Eastern Mediterranean Univ Dept Comp Engn TRNC Gazimagusa Turkey

ISBN: (纸本)0780393635

multiobjective evolutionary optimization has been demonstrated to be an efficient method for some difficult multiobjective optimization problems;particularly the quadratic assignment problem which is a provably difficult NP-complete problem with a multitude of real-world applications. This paper introduces the use of a segment-based external memory in evolutionary multiobjective optimization. In principle, variable-size solution segments taken from a number of previously promising solutions are stored in an external memory whose elements are used in the construction of new solutions. In the construction of a solution, a solution segment is retrieved from the external memory and used in the construction of complete solutions through evolutionary recombination operators. The aim is to provide further intensification around promising solutions without weakening the exploration capabilities. Different instances of the multiobjective quadratic assignment problem are used for performance evaluations and, almost in all trials, the proposed external memory strategy provided significantly better results than the multiobjective genetic algorithm (MOGA).

关键词： computational complexity evolutionary computation optimisation evolutionary multiobjective optimization evolutionary recombination operators multiobjective genetic algorithm multiobjective quadratic assignment problem segment based external memory variab

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Towards a Design Support System for Urban Walkability

Towards a Design Support System for Urban Walkability

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15th Annual International Conference on Computational Science (ICCS)

作者： Blecic, Ivan Cecchini, Arnaldo Trunfio, Giuseppe A. Univ Sassari Dept Architecture Planning & Design Alghero Italy

In the paper we present an urban design support tool centered on pedestrian accessibility and walkability of places. Differently from standard decision support systems developed for the purpose of evaluating given pre-defined urban projects and designs, we address the inverse problem of having the software system itself generate hypotheses of projects and designs, given some (user-provided) objectives and constraints. Taking as a starting point a model for evaluating walkability, we adapt the NSGA-II multi-objective genetic algorithm to produce the front of non-dominated design alternatives to satisfy certain predefined constraints. By way of example, we briefly present an application of the system to a real urban area.

关键词： design support system walkability multiobjective genetic algorithm urban design

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Application of Evolutionary algorithm to multiobjective Optimization of Hydraulic Actuation System

Application of Evolutionary Algorithm to Multiobjective Opti...

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International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

作者： Averchenkov, V. Kazakov, P. Kazakov, V. Reutov, A. Lozbinev, F. Bryansk State Tech Univ Dept Informat Technol Bryansk Russia Bryansk State Tech Univ Power Engn & Elect Dept Bryansk Russia

ISBN: (纸本)9781467381147

An approach to improve the quality of the design hydraulic actuator by means of solving this problem as a multiobjective is suggested. A mathematical model of multiobjective optimization design and operating parameters of the hydraulic actuator is considered. All the set of Pareto-optimal alternatives is obtained for decision making in selection of the best variant of hydraulic actuator. The special genetic algorithm for Pareto-set obtaining was developed.

关键词： hydraulic actuator stability and power intensity of a hydraulic actuator multiobjective optimization Pareto set multiobjective genetic algorithm

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A Multi-Objective genetic algorithm Framework for Design Space Exploration of Reliable FPGA-based Systems

A Multi-Objective Genetic Algorithm Framework for Design Spa...

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2010 IEEE World Congress on Computational Intelligence

作者： Bolchini, Cristiana Lanzi, Pier Luca Miele, Antonio Politecn Milan Dipartimento Elettron Informaz I-20133 Milan Italy

ISBN: (纸本)9781424481262

This paper presents a framework for the design space exploration of reliable FPGA systems based on a multi-objective genetic algorithm (NSGA-II). The framework takes into account several design metrics and outputs a set of Pareto-optimal design solutions. The framework is compared to the multi-objective version of simulated annealing (AMOSA) and it is empirically studied in terms of scalability using three real-world circuits and a set of synthetic problems of different sizes. Our results show that the proposed approach generates a rich set of Pareto-optimal solutions whereas AMOSA tends to find suboptimal solutions. Our empirical scalability analysis shows that, while the problem space is exponential in the number n of functional units constituting the system, the number of required by our framework grows as O (n(3.6)).

关键词： FPGA system Pareto optimal design solution Pareto optimisation design metrix field programmable gate arrays genetic algorithms integrated circuit design multiobjective genetic algorithm real world circuit scalability analysis simulated annealing simulated annealing space exploration design suboptimal solution synthetic problem

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