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 ...
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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.
Several strategies based on geneticalgorithms 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 strategie...
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Several strategies based on geneticalgorithms 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 geneticalgorithm 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.
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 th...
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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.
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 a...
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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.
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-har...
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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 geneticalgorithm approximately by a factor of three. The paper is also validated with the static wavelength assignment problem in optical networks.
This work presents two parallel genetic algorithms ( PGAs) for product configuration management: a parallel conventional geneticalgorithm ( PCGA) and a parallel multiple- searching geneticalgorithm ( PMGA). This par...
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This work presents two parallel genetic algorithms ( PGAs) for product configuration management: a parallel conventional geneticalgorithm ( PCGA) and a parallel multiple- searching geneticalgorithm ( 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 geneticalgorithms ( 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.
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...
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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 PGA is a feasible and effective optimization tool for inverse heat conduction problems.
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...
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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 PGA is a feasible and effective optimization tool for inverse heat conduction problems.
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 propellan...
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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.
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 ...
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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.
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