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General Meeting of the IEEE-Power-and-Energy-Society

作者： Mahdad, B. Srairi, K. Bouktir, T. Univ Biskra Algeria Dept Elect Engn Biskra Algeria Oum Elbouaghi Univ Dept Elect Engn Oum El Bouaghi Algeria

ISBN: (纸本)9781424442409

Under critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the Optimal Power Flow (OPF) under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed tested on IEEE 30-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, ACO) and with two robust simulation packages: PSAT and MATPOWER. The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

关键词： Dynamic control parallel genetic algorithm System loadability FACTS SVC Optimal power flow System security Planning and control

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Computation of Molecular Electronic Structure by genetic algorithm

Computation of Molecular Electronic Structure by Genetic Alg...

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2nd International Conference on Neural Network and genetic algorithms in Materials Science and Engineering

作者： Sharma, Rahul Saha, Rajendra Nandy, Subhajit Bhattacharyya, Shankar Prasad Chaudhury, Pinaki Indian Assoc Cultivat Sci Dept Phys Chem Kolkata 700032 India Univ Calcutta Dept Chem Kolkata India

Several strategies based on genetic algorithms have been explored to locate the ground state energy and structure of atoms and molecules. A variational recipe in a finite basis has been invoked in one of the strategies leading to a matrix eigenvalue problem that has been solved by GA with simultaneous optimization of the basis. In the second approach, the molecular electronic Schrodinger equation has been solved by genetic algorithm by directly optimizing the electronic probability amplitude distribution in space. In the third approach, the single-particle density matrix in atomic orbital basis has been treated as the basic unknown quantity. The search for the optimal ground state density has been simplified by a unitary transformation on the trial density which has been optimized by GA.

关键词： Basis optimization Born-Oppenheimer method Constrained variation Density matrix Density-GA method Direct density method Electronic structure calculation genetic algorithm Nonlinear optimization Optimization parallel genetic algorithm Polythiophene oligomers Rayleigh Quotient minimization Soft computing Su-Schrieffer-Heeger Hamiltonian

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OPF with Envirenmental Constraints with SVC Controller using Decomposed parallel GA: Application to the Algerian Network

OPF with Envirenmental Constraints with SVC Controller using...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Mahdad, B. Bouktir, T. Srairi, K. Univ Biskra Algeria Dept Elect Engn Biskra Algeria Oum Elbouaghi Univ Dept Elect Engn Oum El Bouaghi Algeria

ISBN: (纸本)9781424442409

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria up to 90% of the electricity demand produced by thermal generators (vapor, gas), in order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed parallel GA to solve the multi objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed: the first sub problem related to the active power planning to minimize the total fuel cost, and the second sub problem is a reactive power planning to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed tested on the Algerian 59-bus network test and compared with conventional method and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

关键词： Environmental economic dispatch Dynamic control parallel genetic algorithm multi-objective System loadability FACTS SVC Optimal power flow System security Planning and control

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Migration effects of parallel genetic algorithms on line topologies of heterogeneous computing resources

Migration effects of parallel genetic algorithms on line top...

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20th Workshop on Circuits and Systems

作者： Gong, Yiyuan Guan, Senlin Nakamura, Morikazu Univ Ryukyus Fac Informat Engn Ryukyus Okinawa 9030213 Japan

This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangements. Migration effects are evaluated from the viewpoints of scalability, chromosome diversity, migration frequency and solution quality. The results reveal that the performance of parallel GAs strongly depends on the design of the chromosome migration in which we need to consider the arrangement of heterogeneous computing resources, the migration frequency and so on. The results contribute to provide referential scheme of implementation of parallel GAs on heterogeneous computing resources.

关键词： parallel genetic algorithm grid computing line topology heterogeneous computing resources

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A hybrid parallel genetic algorithm approach for graph coloring

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2005年第3期9卷 249-259页

作者： Sivanandam, S. N. Sumathi, S. Hamsapriya, T. PSG Coll Technol Coimbatore Coimbatore 641004 Tamil Nadu India

Graph-Coloring problem (GCP) deals with assigning labels (colors) to the vertices of a graph such that adjacent vertices do not get the same color. Coloring a graph with minimum number of colors is a well-known NP-hard problem. In this paper a new permutation based representation of graph coloring problem is solved using a parallel genetic algorithm (PGA). Migration model of parallelism is used with Message passing interface (MPI) for implementation of parallel genetic algorithm. Three-crossover operators namely greedy partition crossover (GPX), Uniform independent set crossover (UISX), and Permutation-based crossover (PX) are used. The performance of the three crossover operators is investigated in terms of convergence and execution time for standard benchmark graphs. The results show that GPX performs well in terms of convergence and PX in terms of execution time. The three crossover operators in parallel genetic algorithm outperform the serial genetic algorithm approximately by a factor of three. The paper is also validated with the static wavelength assignment problem in optical networks.

关键词： Graph coloring problem parallel genetic algorithm migration model message passing interface (MPI) static wavelength assignment

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parallel genetic algorithms for product configuration management on PC cluster systems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2007年第11-12期31卷 1233-1242页

作者： Yeh, Jinn-Yi Wu, Tai-Hsi Chang, Ju-Ming Natl Chiayi Univ Dept Management Informat Syst Chiayi 60004 Taiwan Feng Chia Univ Dept Ind Engn & Management Syst Taichung 40724 Taiwan Yuan Ze Univ Dept Ind Engn & Management Chungli 320 Taiwan

This work presents two parallel genetic algorithms ( PGAs) for product configuration management: a parallel conventional genetic algorithm ( PCGA) and a parallel multiple- searching genetic algorithm ( PMGA). This parallel/ distributed approach is based on a coarsegrained ( or island) paradigm which is implemented on a cluster of PCs using message passing interface for the genetic information interchange. The product configuration problem assuming that customers would like to have minimum cost and a customized product can be obtained by finding the shortest path of the configuration network diagram. The performance of these algorithms is estimated by comparing the solutions of PGAs with those of sequential genetic algorithms ( GAs) and mathematical programming. A weighting scale example from an empirical study is reported for illustrational purposes. Computational results show that the solutions obtained from the PMGA outperform other GAs in both accuracy and efficiency.

关键词： parallel genetic algorithm product configuration management PC cluster message passing interface

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parallel genetic algorithms for the solution of inverse heat conduction problems

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2007年第2期84卷 241-249页

作者： Guo, Q. Shen, D. Guo, Y. Lai, C.-H. Wuhan Univ Technol Wuhan 430070 Peoples R China Xiaogan Univ Xiaogan Peoples R China Univ Greenwich Old Royal Naval Coll Sch Comp & Math Sci London SE10 9LS England

A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PG... 详细信息

关键词： heat conduction inverse problem parallel genetic algorithm

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parallel genetic algorithms for the solution of inverse heat conduction problems

Parallel genetic algorithms for the solution of inverse heat...

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Joint Meeting of the International Symposium on Distributed Computing and Applications to Business, Engineering and Science/International Conference on parallel algorithms and Computing Evironments

关键词： heat conduction inverse problem parallel genetic algorithm

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Reconstruction of periodic unit cells of multimodal random particulate composites using genetic algorithms

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COMPUTATIONAL MATERIALS SCIENCE 2008年第2期42卷 352-367页

作者： Kumar, Natarajan Chennimalai Matous, Karel Geubelle, Philippe H. Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA

We develop a procedure for characterization and reconstruction of periodic unit cells of highly filled, multimodal, particulate composites. Rocpack, a particle packing software, is used to generate the solid propellant microstructures and one- and two-point probability functions are used to describe its statistical morphology. The reconstruction is carried out using a parallel Augmented Simulated Annealing algorithm with a novel mutation operator based on a mass-spring system to eliminate overlap and improve the code performance. Results from the reconstruction procedure, for four-phase random particulate composites of 40-70% packing fraction, are detailed to demonstrate the capabilities of the reconstruction model. The presented results suggest good convergence and repeatability of the optimization scheme, even for high volume fractions, and good scalability of the algorithm. (C) 2007 Elsevier B.V. All rights reserved.

关键词： microstructure reconstruction particle packing probability functions parallel genetic algorithm solid propellant

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The Study of Analog Evolvable Chebyshev Transconductance Filters

The Study of Analog Evolvable Chebyshev Transconductance Fil...

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7th World Congress on Intelligent Control and Automation

作者： Li, Yao Zhang, Xuehua Zhu, Li Song, Wenfu Qiao, Shuang Beihua Univ Sch Phys Jilin Jilin Province Peoples R China Northeast Normal Univ Sch Phys Changchun Jilin Province Peoples R China

ISBN: (纸本)9781424421138

An evolvable hardware structure and design method on an analogue evolvable trans-conductance filter was presented. Its own technical parameters could be changed with external environment's real-time changing. The filter has the very good adaptive ability and certain fault-tolerant ability. An improved parallel genetic algorithm with migration strategy based on parallel crossover migration was applied. Selection strategy used an improved expected value model. Crossover strategy selected AND/OR crossover logic operators. Mutation strategy selected dual exclusive NOR/ exclusive OR logic operators. Probability of crossover and mutation used adaptive probability which could be adjusted dynamically by reflecting diversity degrees of a population. A multi-objective optimization fitness function for the filter's parameters had been built. Speed of resource allocation and quality of solutions is improved greatly by improved parallel genetic algorithm. The in-system programmability programmable analog circuits (ispPAC10) were used. The four-order Chebyshev trans-conductance filter is built by cascade method, which is able to meet performance request in its stop-band, passing band and transition band. Its practicality had been simulated and verified. Values of evolutionary parameters are in line with theoretical value exceedingly. Simulation results are satisfying.

关键词： evolvable hardware parallel genetic algorithm multi-objective optimization trans-conductance filter in-system programmability programmable analogue circuits

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