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pso algorithm
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  pso算法
    Results of the two benchmark functions indicate that the PSO algorithm based on the strategy of random inertia weight (RIW) has been significantly improved on both optimization speed and computational accuracy.
    测试表明基于随机惯性权 (RIW )策略的PSO算法 ,其全局搜优的速率与精度有明显提高 .
短句来源
    In ordering to improve its global convergence,the inertia weight of PSO algorithm has adjusted automatically.
    为了提高算法的全局收敛性,对PSO算法中的惯性因子ω执行自适应调节。
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  “pso algorithm”译为未确定词的双语例句
    Empirical Study on Stock Through Arch Based on PSO Algorithm
    基于PSO的证券市场ARCH模型实证研究
短句来源
    From the view of Swarm Intelligence Theory,an evolutionary model based on the method of Particle Swarm Optimization(PSO) is presented,and the PSO algorithm for solving the Nash equilibriums for n persons' non-cooperative game is proposed.
    基于粒子群优化方法从群智能的角度建立了博弈的演化模型,为求解有限n人非合作博弈的纳什均衡设计了一种粒子群优化算法.
短句来源
    In addition,the compute performance of the PSO algorithm is improved by setting the initial particle swarm in the feasible strategy space and controlling the iterative step lengths.
    通过随机初始点的可行化以及对迭代步长的控制,保证粒子群在算法的迭代过程中始终保持在博弈的可行策略空间内,避免了在随机搜索中产生无效的粒子,因此提高了粒子群优化算法求解纳什均衡的计算性能.
短句来源
    The results of simulation indicate that modified PSO algorithm perform better than the genetic algorithm and the heuristic algorithm on the searching speed of global optimum,convergence speed and avoiding prematurity.
    通过仿真实例,改进微粒群算法的全局寻优、收敛性和克服早熟的能力均优于遗传、启发式算法。
短句来源
    Then with the proper setting,the experimental results indicated that QPSO handles integer programming problems much efficiently,and in most cases it converged faster than the PSO algorithm.
    测试函数的仿真结果表明,通过适当的参数设置,并将每次迭代所生成的实数值截至整数值后进行下一次迭代,可以保证QPSO算法求解的精度,提高收敛速度且能有效避免早熟。
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  pso algorithm
The ability of tracking changed PMD using PSO algorithm was also investigated.
      
The Particle Swarm Optimization (PSO) algorithm was introduced in adaptive PMD compensation.
      
Two-stage adaptive PMD compensation in 40 Gb/s OTDM optical communication system using PSO algorithm
      
Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the "the best" ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network.
      
The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO).
      
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Particle Swarm Optimization (PSO) has been successfully applied to many optimization problems on real search space. For integer programming, there exist some difficulties and problems for the direct applications of PSO due to the variables belonging to the set of integers. In this paper, a new PSO algorithm is presented for integer programming, which evolves on the set of Integer space. The results regarding the performance of PSO algorithm on the class of problems are reported.

 针对整数规划问题的特点,提出了一种在整数空间中进行进化计算的PSO算法,使微粒群的进化限于整数空间.仿真实验结果验证了方法的正确性与有效性.

Particle Swarm Optimization(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.Techniques for the application of SPO to the constrained optimization problems are described in the paper.The constrained optimization problem is transformed to unconstrained optimization problem and minmax problem respectively through penalty function.In order to overcome the difficulties of choosing the penalty function,a new PSO algorithm with two particle swarms are presented.the...

Particle Swarm Optimization(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.Techniques for the application of SPO to the constrained optimization problems are described in the paper.The constrained optimization problem is transformed to unconstrained optimization problem and minmax problem respectively through penalty function.In order to overcome the difficulties of choosing the penalty function,a new PSO algorithm with two particle swarms are presented.the first one is for minimizing the objective function and the other for the constrained satisfaction.The simulation results show the correctness and efficiency of the methods.

微粒群算法(简称PSO算法)是一种新型的进化计算方法,已在许多领域得到了非常成功的应用。本文以约束优化问题为对象,首先介绍了采用罚函数法将约束优化问题化为无约束优化问题,和将约束优化问题转化为minmax问题,然后对无约束优化问题和minmax问题,采用PSO算法进行进化求解;在此基础上,以目标函数和约束满足分别为优化目标提出了一种双微粒群的PSO算法。仿真实验结果验证了方法的正确性与有效性。

The effects of inertia weight on particle swarm optimization (PSO) performance are analyzed. A novel method of selecting inertia weight in PSO is developed, which can tune the expectations of inertia weights adaptively when the inertia weights are randomly selected and lead to effectively balance between the local and global search ability. Results of the two benchmark functions indicate that the PSO algorithm based on the strategy of random inertia weight (RIW) has been significantly improved...

The effects of inertia weight on particle swarm optimization (PSO) performance are analyzed. A novel method of selecting inertia weight in PSO is developed, which can tune the expectations of inertia weights adaptively when the inertia weights are randomly selected and lead to effectively balance between the local and global search ability. Results of the two benchmark functions indicate that the PSO algorithm based on the strategy of random inertia weight (RIW) has been significantly improved on both optimization speed and computational accuracy.

分析了惯性权值对粒子群优化 (PSO)算法优化性能的影响 ,进而提出选择惯性权值的新策略 .在随机选取惯性权值的同时 ,自适应地调整随机惯性权值的数学期望 ,有效地调整算法的全局与局部搜索能力 .测试表明基于随机惯性权 (RIW )策略的PSO算法 ,其全局搜优的速率与精度有明显提高 .

 
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