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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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parallel Hybrid genetic algorithm for Solving Design and Optimization Problems

Parallel Hybrid Genetic Algorithm for Solving Design and Opt...

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International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS)

作者： Gladkov, L. A. Gladkova, N., V Semushin, E. Y. Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030392161;9783030392154

The paper considers a problem of building the hybrid algorithm for solving the optimization design tasks on the basis of integration of different methods of computation intelligence. The authors describe the definition and the main approaches to building the hybrid systems and demonstrate the possibilities of integration of the evolutionary design and multi-agent systems methods The different approaches to evolutionary design of the agents are considered. Different methods of parallelizing the computational process and the main models of parallel genetic algorithms, their benefits and shortcomings are described and analyzed in the paper. A hybrid parallel genetic algorithm for searching and optimization of the design decisions is developed in the paper. The algorithm is implemented as software subsystem and investigated in terms of its effectiveness.

关键词： Design tasks Bioinspired algorithms Hybrid methods parallel genetic algorithm Multiagent systems

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An Improved parallel Adaptive genetic algorithm Based on Pareto Front for Multi-Objective Problems

An Improved Parallel Adaptive Genetic Algorithm Based on Par...

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2nd International Symposium on Knowledge Acquisition and Modeling

作者： Liu, Guangyuan Zhang, Jingjun Gao, Ruizhen Shang, Yanmin Hebei Univ Engn Sci Res Off Handan 056038 Hebei Peoples R China

ISBN: (纸本)9780769538884

For multi-objective optimization problems, we introduced IPAGA (Improved parallel Adaptive genetic algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： parallel genetic algorithm adaptive crossover adaptive mutation non-dominated-set Pareto front

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parallel Distributed genetic algorithm for Expensive Multi-Objective Optimization Problems

Parallel Distributed Genetic Algorithm for Expensive Multi-O...

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12th International Conference on Computer Aided Systems Theory (EUROCAST 2009)

作者： Szlachcic, Ewa Zubik, Waldemar Wroclaw Univ Technol Inst Comp Engn Control & Robot 11-17 Janiszewskiego St PL-50372 Wroclaw Poland TETA SA Al PL-53137 Wroclaw Poland

ISBN: (纸本)9783642047718

In many Multi-Objective, Optimization Problems it is required to evaluate it great number of objective functions and constraints and the calculation effort is very high. The use of parallelism in Multi-Objective genetic algorithms is one of the solutions of this problem. In this work we propose an algorithm, based on parallelization scheme using island model with spatially isolated populations. The intent of the proposed paper is to illustrate that modifications made to it selection and resolution processes and to a migration scheme have further improved the efficiency of the algorithm and good distribution of Pareto front.

关键词： multi-objective optimization parallel genetic algorithm multiple resolution island model migration strategy

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A novel parallel quantum genetic algorithm

A novel parallel quantum genetic algorithm

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4th International Conference on parallel and Distributed Computing, Applications and Technologies (PDCAT 2003)

作者： Zhang, GX Jin, WD Hu, LH Natl EW Lab Chengdu 610036 Peoples R China

ISBN: (纸本)0780378407

In this paper, a novel parallel evolutionary algorithm called coarse-grained parallel quantum genetic algorithm (CGPQGA) is proposed. The main points of CGPQGA are that a new chromosome representation called qubit representation, a novel evolutionary strategy called qubit phase comparison approach and an extended version of coarse-grained model called hierarchical ring model are introduced. Based on the concepts and principles of quantum computing and quantum parallelism introduced, CGPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploration and exploitation simultaneously. In CGPQGA, the best individual can be easy to migrate to all processors and communication overhead is much less expensive. The experimental results of infinite impulse response digital filter design demonstrate that CGPQGA can speedup the migration of the top individuals of subpopulations and CGPQGA is superior to other several genetic algorithms greatly in quality and efficiency.

关键词： quantum genetic algorithm parallel genetic algorithm coarse-grained mode IIR digital filter

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A parallel genetic algorithm with Region Division Strategy to Solve Taxi-Passenger Matching Problem

A Parallel Genetic Algorithm with Region Division Strategy t...

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IEEE Symposium Series on Computational Intelligence

作者： Yi-Wen Liu Xin-Yuan Zhang Yue-Jiao Gong Wei-Neng Chen Jun Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China

Taxi-passenger matching plays a crucial role in modern taxi systems. However, currently, the greedy mechanisms are widely adopted, which may limit the quality of services provided by the systems. In this paper, we first formulate the taxi-passenger matching as a global optimization problem by considering the pickup rate and average waiting time of passengers. Then, we propose a parallel genetic algorithm to solve the problem. New operators, including initialization, crossover, and mutation, are designed specifically for the problem. In addition, we use a divide-and-conquer strategy for dimension reduction. The problem is divided into a number of sub-problems according to the geographical locations of passengers and taxis. Each sub-problem is then solved in a parallel way by a sub-component of our proposed algorithm. Experimental results validate the effectiveness and efficiency of the proposed algorithm. It is able to greatly enhance the quality of services provided by the taxi systems.

关键词： Dispatch algorithm Taxi system Global optimization Divide-and-conquer parallel genetic algorithm

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A graphical processing unit-based parallel hybrid genetic algorithm for resource-constrained multi-project scheduling problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第16期33卷 e6266-e6266页

作者： Uysal, Furkan Sonmez, Rifat Isleyen, Selcuk Kursat Ankara Social Sci Univ Grad Sch Social Sci TR-06100 Ankara Turkey Middle East Tech Univ Dept Civil Engn Ankara Turkey Gazi Univ Dept Ind Engn Ankara Turkey

In this article, we present a parallel graphical processing unit (GPU)-based genetic algorithm (GA) for solving the resource-constrained multi-project scheduling problem (RCMPSP). We assumed that activity pre-emption is not allowed. Problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU-based GA, problem is solved together with a CPU and a GPU. The results showed that GPU-based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large-scale problems.

关键词： parallel genetic algorithm resource constrained multi‐ project scheduling problem resource constrained project scheduling problem resource management

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A Novel parallel Quantum genetic algorithm

A Novel Parallel Quantum Genetic Algorithm

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The Fourth International Conference on parallel and Distributed Computing, Applications and Technologies

作者： Gexiang Zhang Weidong Jin Laizhao Hu National EW Laboratory School of Electrical Engineering Southwest Jiaotong University China Electronics Technology Group Corporation No.29 Research Institute

In this paper,a novel parallel evolutionary algorithm called coarse-grained parallel quantum genetic algorithm(CGPQGA) is *** main points of CGPQGA are that a new chromosome representation called qubit representation,a novel evolutionary strategy called qubit phase comparison approach and an extended version of coarse-grained model called hierarchical ring model are *** on the concepts and principles of quantum computing and quantum parallelism introduced, CGPQGA is characterized by rapid convergence,good global search capability and the ability of possessing exploration and exploitation *** CGPQGA, the best individual can be easy to migrate to all processors and communication overhead is much less *** experimental results of infinite impulse response digital filter design demonstrate that CGPQGA can speedup the migration of the top individuals of subpopulations and CGPQGA is superior to other several genetic algorithms greatly in quality and efficiency.

关键词： quantum genetic algorithm parallel genetic algorithm coarse-grained mode IIR digital filter

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Optimizing AutoTVM by parallel genetic algorithms

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2025年第03N04期39卷

作者： Zhao, Changhai Wen, Jiamin Shang, Minqiang Feng, Yuchen Shi, Xiaohua China Natl Petr Corp BGP Inc Natl Engn Res Ctr Oil & Gas Explorat Comp Software Zhuozhou 072751 Peoples R China Beihang Univ Sch Software Beijing 100083 Peoples R China

To make deep neural networks automatically achieve the same or better performance compared with those in hand-optimized libraries, Tensor Virtual Machine (TVM) has combined a genetic algorithm (GA) with its AutoTVM auto-tuning process. The genetic algorithm of TVM has a primary and classic design, with restrictions in terms of searching scope, ability, and efficiency. Meanwhile, the current AutoTVM process is time-consuming. The whole process may last hours on GPUs. As such, we propose a new auto-tuning method that is based on a parallel GA and takes advantage of the strengths of the Roofline model-based cost models and machine learning classification models to widen the search scope and improve search efficiency. The new auto-tuning method achieves double optimization on both tuning results and tuning time. A series of experiments show that the new way improves the inference time of typical deep networks by about 8-14% and speeds up the time consumption of the auto-tuning process up to 1.2-1.52x on GPUs compared with the original GA process of AutoTVM.

关键词： parallel genetic algorithm deep neural networks (DNNs) TVM

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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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