一种改进的差分进化优化算法

2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) Pub Date : 2009-07-11 DOI:10.1109/CASE.2009.116

Jin Huibin, Liu Mingguang

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引用次数: 2

摘要

差分进化(DE)是一种处理不可微、非线性和多模型目标函数的有效方法。但是，在收敛速度和全局优化方面，DE还有很大的提升空间。本文提出了双最优变异操作和混沌微分进化来提高DE算法的优化性能。仿真结果表明，改进的差分进化算法收敛速度快，稳定性强。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Improved Differential Evolution Alogorithm for Optimization

Differential Evolution (DE) is an efficient approach capable of handling non-differentiable, non-linear and multi-model objective functions. However, in convergence speed and global optimization, there is still much room for DE to be improved. In this paper, double best mutation operation and chaos Differential Evolution are proposed to improve DE algorithm’s optimized performance. The simulated cases show modified differential evolution algorithm has rapid convergence speed and strong steadiness.

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2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)

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