An accurate mathematical model has a vital role in controlling and synchronization of different systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. In this paper, an improved shuffled complex evolution (SCE) is proposed for parameter identification of engineering problems. The SCE by employing parallel search efficiently finds neighborhoods of the optimal point. So it carries out exploration in a proper way. But its drawback is due to exploitation stages. The SCE cannot converge accurately to an optimal point, in many cases. The current study focuses to overcome this drawback by inserting a shrinkage stage to an original version of SCE and presents a powerful global numerical optimization method, named the differential SCE. The efficacy of the proposed algorithm is first tested on some benchmark problems. After achieving satisfactory performance on the test problems, to demonstrate the applicability of the proposed algorithm, it is applied to ten identification problems includes parameter identification of ordinary differential equations and chaotic systems. Practical experiences show that the proposed algorithm is very effective and robust so that it produces similar and promising results over repeated runs. Also, a comparison against other evolutionary algorithms reported in the literature demonstrates a significantly better performance of our proposed algorithm.

中文翻译：

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使用差分混洗复杂进化的工程问题参数识别

准确的数学模型在不同系统的控制和同步中起着至关重要的作用。但通常在现实世界的问题中，参数会混杂不匹配和失真。在本文中，提出了一种改进的混洗复杂进化（SCE）用于工程问题的参数识别。SCE 通过采用并行搜索有效地找到了最佳点的邻域。因此，它以适当的方式进行探索。但它的缺点是由于开发阶段。在许多情况下，SCE 无法准确收敛到最佳点。目前的研究重点是通过在原始版本的 SCE 中插入收缩阶段来克服这个缺点，并提出了一种强大的全局数值优化方法，称为微分 SCE。首先在一些基准问题上测试了所提出算法的有效性。在测试问题上取得令人满意的性能后，为证明所提算法的适用性，将其应用于十个辨识问题，包括常微分方程和混沌系统的参数辨识。实践经验表明，所提出的算法非常有效和稳健，因此在重复运行时会产生相似且有希望的结果。此外，与文献中报道的其他进化算法的比较表明，我们提出的算法具有明显更好的性能。它适用于十个辨识问题，包括常微分方程和混沌系统的参数辨识。实践经验表明，所提出的算法非常有效和稳健，因此在重复运行时会产生相似且有希望的结果。此外，与文献中报道的其他进化算法的比较表明，我们提出的算法具有明显更好的性能。它适用于十个辨识问题，包括常微分方程和混沌系统的参数辨识。实践经验表明，所提出的算法非常有效和稳健，因此在重复运行时会产生相似且有希望的结果。此外，与文献中报道的其他进化算法的比较表明，我们提出的算法具有明显更好的性能。