Effects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problem

M. Z. Zakaria, H. Jamaluddin, R. Ahmad, A. Muhaimin
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引用次数: 6

Abstract

The growing interest in multiobjective optimization algorithms and system identification resulted in a huge research area. System identification is about developing a mathematical model for representing the system observed. This paper describes the effects of genetic algorithm parameters used in multiobjective optimization algorithm (MOO) that is applied to system identification problem. Two simulated linear systems with known model structure were considered for representing the system identification problem. The performance metrics used in this study are convergence and diversity metric. These metrics show the performance of MOO when GA parameters are varied. The simulation results show the effects of GA parameter on MOO performance. A right combination of GA parameters used in MOO is shown in this study.
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遗传算法参数对多目标优化算法应用于系统辨识问题的影响
对多目标优化算法和系统辨识的兴趣日益浓厚,形成了一个巨大的研究领域。系统识别是关于开发一个数学模型来表示所观察到的系统。本文讨论了遗传算法参数在多目标优化算法(MOO)中对系统辨识问题的影响。考虑两个模型结构已知的仿真线性系统来表示系统辨识问题。本研究使用的绩效指标为收敛性指标和多样性指标。这些指标显示了当GA参数变化时MOO的性能。仿真结果显示了遗传算法参数对MOO性能的影响。本研究给出了用于MOO的GA参数的正确组合。
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