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algorithm for optimization
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  “algorithm for optimization”译为未确定词的双语例句
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  algorithm for optimization
We analyze the behavior of a class of evolutionary algorithm (EA) known as cellular EA (cEA), and compare it against a tailored neural network model and against a canonical genetic algorithm for optimization of the p-median problem.
      
A genetic algorithm for optimization of neural network capable of learning to search for food in a maze
      
A finite, nonadjacent extreme-point search algorithm for optimization over the efficient set
      
A stochastic algorithm for optimization problems with continua of inequalities
      
A Superlinearly Convergent SSLE Algorithm for Optimization Problems with Linear Complementarity Constraints
      
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An endeavour is made in this paper to present a new and simple ILP algorithm for optimization. It has the most desirable features of robustness and fast convergence. The algorithm is successful in applying it to the same problems arisen in engineering design.

本文试图提出一种新的和简单的整数线性规划最优算法,这种方法具有较强的适用性和快速收敛等令人满意的特点.该算法成功地处理了工程设计中所提出的某些问题,文末给出了计算实例.

In this paper a unified approach to maximum likelihood estimation (MLE) of parameters of statistical distributions by the sequential number theoretic algorithm for optimization (SNTO) proposed by Fang and Wang (1989) is adopted. For illustration of the use of this approach more attention is paid to MLE's of parameters of the Weibull and the beta distributions. A number of examples show that the present method is universally useful and effective.

利用方开泰和王元(1989)提出的序贯数论方法的一个优化算法(SNTO),本文给出求统计分布参数的极大似然估计的一个统一方法。为了说明这个方法的运用,我们集中处理威布尔和贝它分布的参数极大似然估计。许多例子表明,我们的方法是普遍有用和有效的。

This paper presents a new algorithm for optimization problems with nonlinear inequality constricts. At each iteration, the algorithm generates the search direction by solving only one quadratic programming (QP), and then making a simple correction for the solution of the QP, moreover this new algorithm needn't to do searching. The other advantage is that it may not only choose any point in En as a starting point, but also escape from the complex penalty function and diameter. moreover the...

This paper presents a new algorithm for optimization problems with nonlinear inequality constricts. At each iteration, the algorithm generates the search direction by solving only one quadratic programming (QP), and then making a simple correction for the solution of the QP, moreover this new algorithm needn't to do searching. The other advantage is that it may not only choose any point in En as a starting point, but also escape from the complex penalty function and diameter. moreover the iteration point will be a feasible descent sequence whenever some iteration point gets into the feasible region. So we call it subfeasible method.Under mild assumptions,the new algorithm is shown to possess global and two step superlinear convergence.

ASuperlinearlyConvergentCombinedPhaseⅠ-PhaseⅡSubfeasibleMethodJIANJinbao(MathematicsandInformationScienceDepartmentofGuangxiU...

 
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