Based on an improved geneticalgorithm,a parallel genetic algorithm is presented and the skeleton implementing is constituted in this *** van der Laan-Talman algorithm is introduced to the geneticalgorithm to design ...
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Based on an improved geneticalgorithm,a parallel genetic algorithm is presented and the skeleton implementing is constituted in this *** van der Laan-Talman algorithm is introduced to the geneticalgorithm to design convergence criteria objectively and to solve the convergence problem in the later *** parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented through establishing the interface between ADAMS software and the genetic *** results show that the parallel genetic algorithm developed in this paper is efficient.
In this paper we consider a parallel and distributed computation of generic algorithms on loosely-coupled multiprocessor systems. Loosely-coupled ones are more suitable for massively parallel processing and also more ...
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In this paper we consider a parallel and distributed computation of generic algorithms on loosely-coupled multiprocessor systems. Loosely-coupled ones are more suitable for massively parallel processing and also more easily VLSI implementation than rightly-coupled ones. However, communication overhead on parallel processing is more serious for loosely-coupled ones. We propose in this paper a parallel and distributed execution method of geneticalgorithm on loosely-coupled multiprocessor systems of fixed network topologies in which each processor element carries out genetic operations on its own chromosome set and communicates with only the neighbors in order to save communication overhead. We evaluate the proposed method on the multiprocessor systems with ring, torus, and hypercube topologies for benchmark problem instances. From the results, we find that the ring topology is more suitable for the proposed parallel and distributed execution since variety of chromosomes in the ring is kept much more than that in the others. Moreover, we also propose a new network topology called cane which is a hierarchical connection of ring topologies. We show its effectiveness by experimental evaluation.
A hybrid parallelization method composed of a coarse-grained geneticalgorithm (GA) and fine-grained objective function evaluations is implemented on a heterogeneous computational resource consisting of 16 IBM Blue Ge...
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A hybrid parallelization method composed of a coarse-grained geneticalgorithm (GA) and fine-grained objective function evaluations is implemented on a heterogeneous computational resource consisting of 16 IBM Blue Gene/P racks, a single x86 cluster node and a high-performance file system. The GA iterator is coupled with a finite-element (FE) analysis code developed in house to facilitate computational steering in order to calculate the optimal impact velocities of a projectile colliding with a polyurea/structural steel composite plate. The FE code is capable of capturing adiabatic shear bands and strain localization, which are typically observed in high-velocity impact applications, and it includes several constitutive models of plasticity, viscoelasticity and viscoplasticity for metals and soft materials, which allow simulation of ductile fracture by void growth. A strong scaling study of the FE code was conducted to determine the optimum number of processes run in parallel. The relative efficiency of the hybrid, multi-level parallelization method is studied in order to determine the parameters for the parallelization. Optimal impact velocities of the projectile calculated using the proposed approach, are reported.
The graphical Steiner tree problem is a classical combinatorial optimization problem that appears in many practically important applications. This paper presents a new parallel genetic algorithm for solving the proble...
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ISBN:
(纸本)9781424423798
The graphical Steiner tree problem is a classical combinatorial optimization problem that appears in many practically important applications. This paper presents a new parallel genetic algorithm for solving the problem. The presented algorithm is based on binary encoding, used the Distance Network Heuristic for evaluating fitness of individuals and is implemented in parallel using global population model. The results of experiments on the OR-Library tests are reported to show the algorithmpsilas performance in comparison with other metaheuristics for the given problem. The speed-up of the parallel implementation is also discussed.
Two kinds of parallel genetic algorithm (PGA) are implemented in this paper based on the MATLAB parallel Computing Toolbox and Distributed Computing Server software. parallel for-loops, SPMD (Single Program Multiple D...
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Two kinds of parallel genetic algorithm (PGA) are implemented in this paper based on the MATLAB parallel Computing Toolbox and Distributed Computing Server software. parallel for-loops, SPMD (Single Program Multiple Data) block and co-distributed arrays, three basic parallel programming modes in MATLAB are employed to accomplish the global and coarse-grained PGAs. To validate and compare our implementation, both PGAs are applied to run the problem of range image registration. A set of experiments have illustrated that it is convenient and effective to use MATLAB to parallelize the existing algorithms. At the same time, a higher speed-up and performance enhancement can be obtained obviously.
Synonyms and the strong association of semantic information increase the dimension text feature vectors, and greatly affect the efficiency and accuracy of text classification. In order to reduce the dimension of the t...
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ISBN:
(纸本)9781784660543
Synonyms and the strong association of semantic information increase the dimension text feature vectors, and greatly affect the efficiency and accuracy of text classification. In order to reduce the dimension of the text feature vectors, this paper presents an improved parallel genetic algorithm to solve the text feature clustering problem. Firstly, a K-means algorithm is used to perform thick-granularity clustering for feature words. Successively, a parallel genetic algorithm is used to perform thin-granularity clustering for feature words. In the process of applying geneticalgorithms, the crossover operator is improved so that the algorithm has a global search ability and local search capability, and reduces the dependence on the initial cluster centers. Finally, feature words in each cluster are analyzed and compressed to form a feature word set which reflects the feature of text classes and semantic information. The experiments validate that our method for text feature extraction is effective.
Multiprocessor task scheduling is one of the hardest combinatorial optimization problems in parallel and distributed systems. It is known as NP-hard and therefore, scanning the whole search space using an exact algori...
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ISBN:
(纸本)9781509006809
Multiprocessor task scheduling is one of the hardest combinatorial optimization problems in parallel and distributed systems. It is known as NP-hard and therefore, scanning the whole search space using an exact algorithm to find the optimal solution is not practical. Instead, metaheuristics are mostly employed to find a near-optimal solution in a reasonable amount of time. In this paper, a multi-population based parallel genetic algorithm is presented for the optimization of multiprocessor task scheduling in the presence of communication costs. To the best of our knowledge, this parallel genetic algorithm approach is applied to the problem at hand for the first time using a benchmark set that includes well-known task graphs from different sources. Our experiments conducted on several task graphs with different sizes from the benchmark set showed the superiority of the approach over a conventional geneticalgorithm and the related works in the literature in terms of two different performance metrics. Our parallel implementation not only decreased the execution time but also increased the quality of the scheduling solutions considerably.
The network coding technique is promising for saving bandwidth in multicast-based applications, and how to design multicast network topologies that are suite for efficiently supporting network coding becomes an im...
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The network coding technique is promising for saving bandwidth in multicast-based applications, and how to design multicast network topologies that are suite for efficiently supporting network coding becomes an important issue at present. In this paper, we at first formulate this problem as a special case of kconnected problem and then deal it with a parallelgeneticalgorithm.
In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic geneticalgorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of max...
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In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic geneticalgorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function) .And we acquire the factors influencing the master-slave parallel genetic algorithm by deriving from the analysis of test data. The experimental data show that the balanced hardware configuration and software design optimization can improve the performance of system in the complexity of the computing environment using the master-slave parallel genetic algorithms.
Based on the combination of NSGA-II algorithm and parallel genetic algorithm,this paper presents a parallel genetic algorithm for multi-objective optimization(PNSGA).At the evolving process of this new algorithm,an in...
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Based on the combination of NSGA-II algorithm and parallel genetic algorithm,this paper presents a parallel genetic algorithm for multi-objective optimization(PNSGA).At the evolving process of this new algorithm,an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set;at the same time,an individual update strategy is introduced to keep the diversity of Pareto optimal *** show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.
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