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International Conference on parallel and Distributed Processing Techniques and Applications

作者： Rashid, H Qureshi, K COMSATS Inst Informat Technol Dept Comp Sci Abbattabad Pakistan

ISBN: (纸本)1932415262

This paper proposed an enhancement in Modified Global (MG) parallel genetic model and new proposed model is called Trigger Model (TM). The parallel GM and TM model performance were compared with sequential model. The performance of MG, TM and sequential model were evaluated on cluster of PCs. The parallel Virtual Machine (PVM) was used a tool. The proposed model (TM) shows a better performance as compared to MG and sequential models.

关键词： parallel genetic algorithm parallel virtual machine performance evaluation

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Self Organized parallel genetic algorithm to Automatically Realize Diversified Convergence

Self Organized Parallel Genetic Algorithm to Automatically R...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI)

作者： Zhang, Li Feng Zhou, Chen Xi Renmin Univ China Sch Informat Beijing 100872 Peoples R China

ISBN: (纸本)9781467315098

In multimodal optimization, maintaining population diversity is one of the most critical issues in genetic algorithm design. A number of niching techniques have been developed and successfully applied to cope with this problem. For multi-population based parallel genetic algorithms, nevertheless, these approaches are obviously inapplicable, since it is very difficult to obtain global information about entire population during parallel evolution procedure. In the present study, a new island model is proposed to overcome this problem. The new method indiscriminately directs local GAs search with considering the topological information of island model. It only uses local information obtained from a few neighbouring subpopulations to achieve a global population diversification. In the new island model, subpopulations are automatically allocated to different regions of searching space so that they could locate multiple optima including both global optima and local optima, simultaneously orders these found optima according to the connection topology of islands, and keeps them until the end of evolution. In addition, through using the proposed method, the performance of PGA is also improved and displays an enhanced global searching capability. Finally, experimental studies, in both unconstrained optimization and combinatorial optimization, are employed to demonstrate the performance of the new island model.

关键词： parallel genetic algorithm Island model Niching Multimodal optimization

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Simulated annealing parallel genetic algorithm based on building blocks migration

Simulated annealing parallel genetic algorithm based on buil...

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7th International Conference on Computational Science (ICCS 2007)

作者： Li, Zhiyong Zhu, Xilu Hunan Univ Sch Comp & Commun Changsha 410082 Peoples R China

ISBN: (纸本)9783540725893

Through analyzing schema theorem and building blocks theory, propose parallel genetic algorithms based on building blocks migration. Relying on convergence condition, receive building blocks from other populations. Using simulated annealing method prevent the density of good schema to increase greatly which will result in premature convergence. Theory analysis and experimental results show that the method not only reduce ineffective migration and decrease communication costs, but also lower the possibility of occurring premature and assure the capability of global convergence.

关键词： parallel genetic algorithm schema theorem building blocks theory simulated annealing migration strategy

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A parallel genetic algorithm for the Job Shop Scheduling Problem

A Parallel Genetic Algorithm for the Job Shop Scheduling Pro...

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12th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Nguyen Huu Mui Vu Dinh Hoa Luc Tri Tuyen Hanoi Univ Educ Dept Informat Technol Hanoi 010000 Vietnam Vietnam Acad Sci & Technol Inst Informat Technol Hanoi 010000 Vietnam

ISBN: (纸本)9781467356046

This paper presents a parallel genetic algorithm for the job shop scheduling problem (JSP). There are following innovations in this new algorithm: active schedules are created by the priority rules of Giffler and Thompson [1];the mutation uses neighborhood searching techniques;the crossover uses GT algorithm and is performed on 3 parents. We illustrate this new method on the parameters of Muth and Thompson's benchmark problems. it can produce optimal solutions at a high percentage of accuracy. Our proposed method is preeminent in comparison with other methods on both the calculation time and the speed of finding optimal solutions.

关键词： job shop scheduling parallel genetic algorithm

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Shape optimization using parallel genetic algorithm and multilevel parameterization algorithm

Shape optimization using parallel genetic algorithm and mult...

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Symposium on Non-Grid-Connected Wind Power

作者： Wang, Guangwen Zhou, Zhenggui Hu, Jun Nanjing Univ Aeronaut & Astronaut Energy & Power Inst Nanjing 210016 Peoples R China

ISBN: (纸本)9780979847110

This paper presents an adaptive multilevel parameterization algorithm based on genetic optimization algorithm in view of the design issues on blade profile and airfoil. According to the principle of multi-grid method, this algorithm using the recursive algorithm of Bezier curve transforms the design variables and their numbers between upper-lower levels saves the prefect individuals. from upper level to lower level in the optimization iterative process. The optimization efficiency is greatly improved by the advancement of simple genetic algorithm, and a local parallel network optimization platform according to the property of genetic algorithm is built, which leads to less time cost. An example on curve transformation and the other on blade profiles optimization are given in the end. The results indicate that the adaptive multilevel parameterization algorithm can obviously accelerate convergence of the iterative, especially, a small number of individuals.

关键词： shape optimization multilevel parameterization algorithm parallel genetic algorithm Bezier curve recursive algorithm

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Simple parallel genetic algorithm Using Cloud Computing

Simple Parallel Genetic Algorithm Using Cloud Computing

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Zhao Jian Feng Zeng Wen Hua Li Guang Ming Liu Min Xiamen Univ Dept Cognit Sci Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9783037852828

Cloud computing is a novel parallel platform, this paper proposed a kind of simple parallel genetic algorithm (PGA) using Cloud computing called SMRPGA. Comparing with the traditional PGAs using high performance computers (HPC), cluster or Grid, SMRPGA is simple and easy to be implemented. Another advantage is that PGA using Cloud computing is easy to be extend to larger-scale, which is very useful for solving the time-consuming problems. A prototype is implemented based on Hadoop, which is an open source Cloud computing. The result of running two benchmark functions showed that the speed-up of PGA using Cloud Computing is not obvious considering the long communication time and it is suitable to solve the time-consuming problems.

关键词： Cloud computing parallel genetic algorithm MapReduce

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A parallel genetic algorithm in Multi-objective Optimization

A Parallel Genetic Algorithm in Multi-objective Optimization

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21st Chinese Control and Decision Conference

作者： Wang Zhi-xin Ju Gang Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China

ISBN: (纸本)9781424427239

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 set. Data 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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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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Bi-clustering of Gene Expression Microarray using Coarse grained parallel genetic algorithm(CgPGA) with Migration 12

Bi-clustering of Gene Expression Microarray using Coarse gra...

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12 IEEE Int C Elect Energy Env Communications Computer Control

作者： Laishram, Ayangleima Vipsita, Swati IIIT Bhubaneswar Dept Comp Sc Engn Bhubaneswar 751003 Orissa India

ISBN: (纸本)9781467365406

Bi-clustering of gene expression micro array data deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Hi-clustering aims at identifying several bi-clusters that reveal potential local patterns from a microarray matrix. In this paper, evolutionary algorithm is used to find bi-clusters of large size which have mean squared residue less than a given threshold, delta. Attention is also given to find bi-clusters with minimum overlapping among themselves by assigning weights to the elements of microarray matrix. Initially, genetic algorithm (GA) is implemented to derive bi-clusters from microarray matrix. From numerical simulations, it is observed that GA took too much time to converge so as to meet the stopping criteria. To further improve the performance of GA, parallel GA (PGA) is implemented with an objective, so as to efficiently handle the problem of slow convergence encountered in traditional GA. A framework of Coarse grained parallel genetic algorithm (CgPGA) for bi-clustering is implemented in this paper. The results obtained from CgPGA are quite encouraging as CgPGA took very less time to meet the stopping criteria. The bi-clusters derived by CgPGA are larger in size, which is one of the primary objective of bi-clustering problem. The experiment was performed on micro array dataset i.e. yeast Saccharomyces cerevisiae cell cycle.

关键词： Bi-clustering genetic algorithm parallel genetic algorithm Mean squared residue Volume Row variance

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A parallel genetic algorithm for Shortest Path Routing Problem

A Parallel Genetic Algorithm for Shortest Path Routing Probl...

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International Conference on Future Computer and Communications (ICFCC 2009)

作者： Yussof, Salman Razali, Rina Azlin See, Ong Hang Department of System and Networking Malaysia Department of Electronic and Communication Engineering Malaysia

ISBN: (纸本)9780769535913

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. This algorithm is developed and run on an MPI cluster. Based on experimental result, there is a tradeoff between computation time and the result accuracy. However, for the same level of accuracy, the proposed parallel algorithm can perform much faster compared to its non-parallel counterpart.

关键词： component Coarse grained message passing interface parallel genetic algorithm shortest path routing

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