解决终端最优控制问题的进化Сomputation

A. Diveev
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引用次数: 0

摘要

本文研究了末端最优控制问题的数值解问题。给出了终端最优控制问题的一般描述及其求解方法的简要概述。通过直接逼近和简化最优控制问题为有限维优化问题,无论控制函数的近似类型如何,目标泛函在期望参数空间上都可能不具有单峰性。因此,建议采用进化算法来解决该问题。提出了一种解决进化计算算法中终端最优控制问题的一般方法。本文介绍了一些被认为是解决最优控制问题最有效的进化算法。提出了一种基于多种进化算法组合的混合进化算法。计算实验考虑终端最优控制问题,采用已知的经典数值方法,利用目标函数的梯度进行搜索,找到最优解。将经典方法与进化方法的结果从函数值和计算代价两方面进行比较,可以看出进化算法能够有效地解决终端最优控制问题
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Evolutionary Сomputation for Solving the Terminal Optimal Control Problem
The present article considers the problem of numerical solution of the terminal optimal control problem. The general statement of the terminal optimal control problem and a brief overview of its solving methods are presented. With a direct approach and reduction of the optimal control problem to the finite-dimensional optimization problem, the target functional on the space of desired parameters, regardless of the type of approximation of the control function, may not have the unimodal property. Therefore, it is advisable to use evolutionary algorithms to solve the problem. A general approach to solving the terminal optimal control problem of evolutionary computational algorithms is presented. The paper presents a description of some evolutionary algorithms that were selected as the most effective for solving the optimal control problem. A hybrid evolutionary algorithm based on a combination of several evolutionary algorithms is considered. The computational experiment considers the terminal optimal control problems, for which optimal solutions were found by known classical numerical methods that use the gradient of the target functionality when searching. Comparison of the results obtained by classical and evolutionary methods by functional values and computational costs allows us to conclude that evolutionary algorithms are able to effectively solve the terminal optimal control problems
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
40
期刊介绍: The journal is aimed at publishing most significant results of fundamental and applied studies and developments performed at research and industrial institutions in the following trends (ASJC code): 2600 Mathematics 2200 Engineering 3100 Physics and Astronomy 1600 Chemistry 1700 Computer Science.
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