UNCONSTRAINED REAL VALUED OPTIMIZATION BASED ON STOCHASTIC DIFFERENTIAL EQUATIONS

Viktor Vegh; Quang M. Tieng

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UNCONSTRAINED REAL VALUED OPTIMIZATION BASED ON STOCHASTIC DIFFERENTIAL EQUATIONS

机译：基于随机微分方程的无约束实值优化

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摘要

A new approach of finding optimal variables to continuous, real valued functions is outlined. The technique is simple to implement, and does not require multiple instances of the variables or knowledge of the cost function derivative. The variable update is governed by the numerical solution to a specific stochastic differential equation. A number of parameters can be changed to alter the manner in which the variables are updated. Different parameter choices lead to changes in the rate and manner of convergence to the optimal solution. Test functions are used to establish method performance. Results show that the method is competitive with established approaches.

机译：概述了一种为连续，实值函数找到最佳变量的新方法。该技术易于实现，不需要变量的多个实例或成本函数导数的知识。变量更新取决于特定随机微分方程的数值解。可以更改许多参数，以更改更新变量的方式。选择不同的参数会导致收敛到最优解的速率和方式发生变化。测试功能用于建立方法性能。结果表明该方法与既定方法具有竞争性。

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来源
《International Journal of Innovative Computing Information and Control》 |2011年第11期|p.6235-6246|共12页
作者
Viktor Vegh; Quang M. Tieng;
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作者单位

Centre for Advanced Imaging University of Queensland Brisbane QLD 4072, Australia;

Centre for Advanced Imaging University of Queensland Brisbane QLD 4072, Australia;

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原文格式 PDF
正文语种 eng
中图分类
关键词
optimization; globally convergent; stochastic differential equation; evolutionary computing;

机译：优化;全球趋同;随机微分方程进化计算;

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机译：基于随机微分方程的全局优化新方法

UNCONSTRAINED REAL VALUED OPTIMIZATION BASED ON STOCHASTIC DIFFERENTIAL EQUATIONS

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