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Two new parallel algorithms based on QPSO

机译:基于QPSO的两个新的并行算法

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摘要

Based on the analysis of classical particle swarm optimization (PSO) algorithm, we adopted Sun's theory that has the behavior of quantum particle swarm optimization (QPSO) algorithm, by analyzing the algorithm natural parallelism and combined with parallel computer high-speed parallelism, we put forward a new parallel with the behavior of quantum particle swarm optimization (PQPSO) algorithm. On this basis, introduced the island model, relative to the fine-grained has two quantum behavior of particle swarm,m optimization algorithm, the proposed two kinds of coarse-grained parallel based on multiple populations has the behavior of quantum particle swarm optimization (QPSO) algorithm. Finally under the environment of MPI parallel machine using benchmark functions to do the numerical test, and a comparative analysis with other optimization algorithms. Results show that based on the global optimal value is superior to the exchange of data based on local optimum values of exchange, but in the comparison of time is just the opposite.
机译:基于经典粒子群优化(PSO)算法的分析,我们采用Sun的理论,具有量子粒子群优化(QPSO)算法的行为,通过分析算法自然并行和与平行计算机高速并行性相结合,我们放置与量子粒子群优化(PQPSO)算法的行为前进新并行。在此基础上,引入了岛式模型,相对于细粒度具有两个量子行为的粒子群,M优化算法,提出了基于多个群体的两种粗粒并行,具有量子粒子群优化的行为(QPSO ) 算法。最后在MPI并联机器的环境下使用基准函数进行数值测试,以及其他优化算法的比较分析。结果表明,基于全局最优值优于基于局部最佳交换价值的数据交换,但在比较的比较中就是相反的。

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