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A multiobjective Coverage-Based Model for Civilian Search and Rescue

NAVAL RESEARCH LOGISTICS 2011年第3期58卷 167-179页

作者： Asiedu, Yaw Rempel, Mark Def Res & Dev Canada Ctr Operat Res & Anal Ottawa ON Canada

The Civil Air Search and Rescue Association (CASARA) is a Canada-wide volunteer aviation association that provides air search support services to the Canadian National Search and Rescue (SAR) program. As with any emergency service provider, the locations of CASARA units greatly impact their overall effectiveness. In this article, the optimal location of CASARA units is formulated as a multiobjective maximal covering location problem. The model addresses the objectives of maximizing the coverage, minimizing the number of units, and maximizing the backup coverage of SAR incidents within Canada. A multigender genetic algorithm is proposed to determine a set of nondominated CASARA location configurations. Results are compared with solutions found using commercial integer programming software. It is shown that the nondominated genetic algorithm solutions are near-optimal. These are determined in much less time than comparable solutions using commercial integer programming software. (C) 2009 Wiley Periodicals, Inc.*Naval Research Logistics 58: 167-179, 2011

关键词： search and rescue covering model multiobjective genetic algorithm

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Hierarchical component-based representations for evolving microelectromechanical systems designs

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2011年第1期25卷 41-55页

作者： Zhang, Ying Agogino, Alice M. Georgia Inst Technol Sch Elect & Comp Engn Savannah GA 31407 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

关键词： Component-Based Genotype Representation Microelectromechanical System Design multiobjective genetic algorithm

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Energy-Aware Partitioning for On-Chip Bus Architecture using a Multi-Objective genetic algorithm

Energy-Aware Partitioning for On-Chip Bus Architecture using...

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International Symposium on VLSI Design, Automation and Test (VLSI-DAT)

作者： Chiou, Lih-Yih Chen, Yi-Siou Jian, Ya-Lun Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

ISBN: (纸本)9781424484997

Incorporating power management during partitioning at the system level contributes considerably to energy efficient architecture. Designers commonly implement systems as a mix of partitioning blocks of various sizes, connected using bus interconnection architecture. Therefore, the use of a partitioning approach that partitions a system with the greatest possible idle time on a dedicated interconnection architecture has become unpractical for power management development. This work presents a novel energy-aware hardware clustering algorithm with a performance estimator for on-chip bus based architectures during high level synthesis, to enhance the quality of solutions for implementing power management systems. Experimental results obtained in four cases reveal that the proposed strategy generates a wide range of cost-effective solutions and is highly effective for today's hardware systems.

关键词： algorithm design and analysis Clustering algorithms Computer architecture Energy consumption genetic algorithms Partitioning algorithms Power demand energy efficient architecture energy-aware hardware clustering energy-aware partitioning genetic algorithms high level synthesis high level synthesis integrated circuit interconnections interconnection architecture multiobjective genetic algorithm on-chip bus architecture on-chip bus based architecture power management systems

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Selecting Syntactic Attributes for Authorship Attribution

Selecting Syntactic Attributes for Authorship Attribution

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International Joint Conference on Neural Networks (IJCNN)

作者： Varela, Paulo Justino, Edson Oliveira, Luiz S. PUCPR Curitiba Parana Brazil UFPR Dept Informat Curitiba Parana Brazil

ISBN: (纸本)9781424496365

In this work we present a methodology to select syntactic attributes for authorship attribution. The approach takes into account a multi-objective genetic algorithm and a Support Vector Machine classifier and it operates in a wrapper mode. Through a series of comprehensive experiments on a database composed of 3000 short articles written in Portuguese we show that the proposed methodology is able to provide a concise subset of attributes, which increases the recognition rate in about 15 percentage points.

关键词： Databases Error analysis Feature extraction genetic algorithms Support vector machines Syntactics attribute subset authorisation authorship attribution database management system genetic algorithms multiobjective genetic algorithm pattern classification recognition rate support vector machine classifier support vector machines syntactic attribute

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Evaluated Preference genetic algorithm and Its Engineering Applications

Evaluated Preference Genetic Algorithm and Its Engineering A...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Liu, Tung-Kuan Chang, Hsin-Yuan Wu, Wen-Ping Chen, Chiu-Hung Ho, Min-Rong Takming Univ Sci & Technol Dept Insurance & Financial Management Taipei Taiwan Takming Univ Sci & Technol Dept Insurance & Finacial Management Taipei Taiwan Takming Univ Sci & Technol Dept Business Adm Taipei Taiwan

ISBN: (纸本)9783037850176

This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference genetic algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.

关键词： EPGA multiobjective genetic algorithm engineering optimization

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MULTI-OBJECTIVE RECONFIGURABLE CONTROLLER DESIGN FOR THREE-TANK SYSTEM USING PARAMETRIC EIGENSTRUCTURE ASSIGNMENT

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CONTROL AND INTELLIGENT SYSTEMS 2011年第1期39卷 12-22页

作者： Sutha, Subbian Thyagarajan, Thangavelu Department of Instrumentation Engineering Madras Institute of Technology Chennai 600044 Tamil Nadu India

The reconfigurable controller (RC) design of strongly interacting multi-input multi-output system is a challenging problem for the designer. In this paper, a parametric right eigenstructure assignment based multiobjective dynamic reconfigurable controller through state feedback is proposed against partial actuator failure for a benchmark three-tank system. The practical aspects of controller design require the conflicting objectives of robust stability to parameter perturbation and smaller controller gain to improve the transient response of the system. These design objectives rise to the minimum condition number of closed-loop eigenvector and minimum norm of feedback controller gain of closed-loop system respectively. Fast and elitist multiobjective genetic algorithm known as nondominated sorting genetic algorithm-II is successfully applied to select the pareto-optimal eigenstructure of closed-loop system. Further, RC has been designed to accommodate partial actuator failure which is considered as loss of control effectiveness in actuators. Added value of this work, the controller uses complex-valued chromosomes. The effectiveness of the proposed controller is illustrated for different actuator fault scenarios through simulation and its performance is analysed.

关键词： Parametric eigenstructure assignment reconfigurable controller multiobjective genetic algorithm nondominated sorting genetic algorithm multiobjective dynamic reconfigurable controller

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Combining multiobjective and Single-objective genetic algorithms in Heterogeneous Island Model

Combining Multiobjective and Single-objective Genetic Algori...

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2010 IEEE World Congress on Computational Intelligence

作者： Pilat, Martin Neruda, Roman Charles Univ Prague Fac Math & Phys Dept Theoret Comp Sci & Math Log Prague 1 Czech Republic Acad Sci Czech Republ Inst Comp Sci Prague 8 Czech Republic

ISBN: (纸本)9781424481262

The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.

关键词： genetic algorithms heterogeneous island model multiobjective genetic algorithm multiobjective optimization problem parallel hybrid algorithm single-objective genetic algorithm

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Global optimization of an accelerator lattice using multiobjective genetic algorithms

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2009年第1期609卷 50-57页

作者： Yang, Lingyun Robin, David Sannibale, Fernando Steier, Christoph Wan, Weishi Univ Calif Berkeley Lawrence Berkeley Lab Adv Light Source Berkeley CA 94720 USA

Storage ring lattice design is a highly constrained multiobjective optimization problem. The objectives can include lattice functions or derived quantities like emittance, brightness, or luminosity while simultaneously fulfilling constraints such as linear stability of the lattice. In this paper we explore the use of multiobjective genetic algorithms (MOGA) to find globally optimized lattice settings in a storage ring. Using the Advanced Light Source (ALS) for illustration, three examples of MOGA are shown and analyzed-(i) using three fit parameters to optimize the straight section betatron function and the natural emittance, (ii) using three fit parameters to optimize the photon brightness of bending magnet and insertion device source points in the lattice and (iii) a six parameter fit creating alternating high and low horizontal betatron functions in subsequent straight sections while still minimizing the natural emittance. Making use of one of the main benefits. of MOGA, we also study the trade-offs in the optimization objectives between sets of optimal solutions. Published by Elsevier B.V.

关键词： Optimization Lattice design Emittance Brightness multiobjective genetic algorithm

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multiobjective scheduling for semiconductor manufacturing plants

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COMPUTERS & CHEMICAL ENGINEERING 2010年第4期34卷 555-566页

作者： Senties, O. Baez Azzaro-Pantel, C. Pibouleau, L. Domenech, S. Univ Toulouse Lab Genie Chim UMR 5503 ENSIACET INPT 4 Allee Emile MonsoBP 44362 F-31432 Toulouse 4 France

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment. (C) 2010 Elsevier Ltd All rights reserved

关键词： Discrete event simulation Artificial neural networks multiobjective genetic algorithm Semiconductor manufacturing

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Scheduling Renewal of Water Pipes While Considering Adjacency of Infrastructure Works and Economies of Scale

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2010年第5期136卷 519-530页

作者： Nafi, Amir Kleiner, Yehuda Natl Res Council Canada Inst Res Construct Ottawa ON K1A 0R6 Canada Cemagref Engees Joint Res Unit Publ Util Management F-67070 Strasbourg France

Much research effort has been dedicated to the development of optimal strategies for rehabilitation and/or replacement of water mains. Some of the methods are intended for high-level planning of groups or cohorts of pipes, while others address low-level scheduling of individual water mains. This paper focuses on the latter aspect. An approach is proposed for the efficient scheduling of individual water mains for replacement in a short to medium predefined planning period and subject to various budgetary constraints. This approach also accounts for economies of scale considerations as well as harmonization with other known infrastructure works. A multiobjective genetic algorithm scheme is used as a tool to search a vast combinatorial solution space, comprising various combinations of pipe replacement schedules.

关键词： Water mains Pipes Renewal planning multiobjective genetic algorithm Roadwork Economies of scale

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