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A parallel and distributed genetic algorithm on loosely-coupled multiprocessor systems

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第4期E81A卷 540-546页

作者： Matsumura, T Nakamura, M Okech, J Onaga, K Univ Ryukyus Dept Informat Engn Okinawa 9030213 Japan Jomo Kenyatta Univ Agr & Technol Dept Math & Comp Sci Nairobi Kenya

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 genetic algorithm 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.

关键词： parallel genetic algorithm distributed genetic algorithm loosely-coupled multiprocessor system network topology cone topology

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A hybrid, massively parallel implementation of a genetic algorithm for optimization of the impact performance of a metal/polymer composite plate

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2013年第2期27卷 217-227页

作者： Narayanan, Kiran Mora, Angel Allsopp, Nicholas El Sayed, Tamer King Abdullah Univ Sci & Technol Thuwal 239556900 Saudi Arabia Cray Comp Stuttgart Germany

A hybrid parallelization method composed of a coarse-grained genetic algorithm (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.

关键词： parallel genetic algorithm computational steering optimization impact ductile fracture composite

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Solving graphical Steiner tree problem using parallel genetic algorithm

Solving graphical Steiner tree problem using parallel geneti...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Nguyen Viet Huy Nguyen Duc Nghia Department of Computer Science Faculty of Information Technology Hanoi University of Technology Hanoi Vietnam

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.

关键词： Steiner tree problem parallel genetic algorithm

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The Implementation and Comparison of Two Kinds of parallel genetic algorithm Using Matlab

The Implementation and Comparison of Two Kinds of Parallel G...

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The Ninth International Symposium on Distributed Computing and Applications to Business,Engineering and Science

作者： Li Nan 1, 2 , Gao Pengdong 1 , Lu Yongquan 1 , Yu Wenhua 1 1. High Performance Computing Center, Communication University of China, Beijing, 100024, China 2. Information Engineering School, Communication University of China, Beijing 100024, China

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.

关键词： parallel genetic algorithm MATLAB distributed computing parallel programming

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Research on text feature clustering based on an improved parallel genetic algorithm

Research on text feature clustering based on an improved par...

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2014 International Conference on Computer Science and Systems Engineering（CSSE2014）

作者： M.Y.Jiang X.J.Fan Y.X.Zhou J.Lian J.Q.Jiang Z.L.Pei College of Computer Science and Technology Inner Mongolia University for the Nationalities College of Mechanical Engineering Inner Mongolia University for the Nationalities

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 genetic algorithms, 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.

关键词： parallel genetic algorithm k-means clustering feature extraction text feature words

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A Multi-Population Based parallel genetic algorithm for Multiprocessor Task Scheduling with Communication Costs

A Multi-Population Based Parallel Genetic Algorithm for Mult...

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IEEE Symposium on Computers and Communications

作者： Rashid Morady Deniz Dal Faculty of Engineering Computer Engineering Department Ataturk University

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 genetic algorithm 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.

关键词： Directed Acyclic Graph DAG Task Graph Multiprocessor Scheduling Task Scheduling Communication Cost Multi-population parallel genetic algorithm Island Model MPI

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Network Coding Oriented Topology Design Based on parallel genetic algorithm

Network Coding Oriented Topology Design Based on Parallel Ge...

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The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Jinkai Li Yun Pan Department of Computer Communication University of China Beijing China

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 parallel genetic algorithm.

关键词： multicast network coding parallel genetic algorithm topology design

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The Design and Implementation of MPI Master-Slave parallel genetic algorithm

The Design and Implementation of MPI Master-Slave Parallel G...

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2012 4th International Conference on Education Technology and Computer(ICETC 2012)

作者： Shuping LIU 1 and Yanliu CHENG 2 1 North China Electric Power University,HeBei, Baoding, China 2 North China Electric Power University,HeBei, Baoding, China

In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm 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.

关键词： MPI Cluster parallel genetic algorithm

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A parallel genetic algorithm in Multi-objective Optimization

A Parallel Genetic Algorithm in Multi-objective Optimization

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2009中国控制与决策会议

作者： WANG Zhi-xin,JU Gang School of Energy and Environment,Southeast University,Nanjing 210096,China

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.

关键词： Multi-objective optimization NSGA-II parallel genetic algorithm Individual migration Individual update

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An Approach for Cloud Resource Scheduling Based on parallel genetic algorithm

An Approach for Cloud Resource Scheduling Based on Parallel ...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Zhongni Zheng,Rui Wang,Hai Zhong,Xuejie Zhang Department of Computer Science and Engineering, Yunnan University,Kunming,P.R.China,650091

Resource scheduling is a key process for clouds such as Infrastructure as a Service *** make the most efficient use of the resources,we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling *** investigate the possibility to place the Virtual Machines in a flexible way to improve the speed of finding the best allocation on the premise of permitting the maximum utilization of resources. Mathematically,we consider the scheduling problem come down to an Unbalance Assignment *** scheduling policy achieved by parallel genetic algorithm which is much faster than traditional genetic *** experiments show that our method improved both the speed of resources allocation and the utilization of system resource.

关键词： resources scheduling parallel genetic algorithm cloud computing assignment problems

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