This paper proposes a new version of the shuffled complex evolution (SCE) algorithm for solving parameter identification problems. The SCE divides a population into several parallel subsets called complex and then improves each sub-complex through an evolutionary process using a Nelder–Mead (NM) simplex search method. This algorithm applies its evolutionary process only on the worst member of each sub-complex whereas the role of other members is not operative. Therefore, the number and variety of search moves are limited in the evolutionary process of SCE. The current study focuses to overcome this drawback by proposing a corporate SCE (CSCE). This algorithm provides an evolutionary possibility for all members of a sub-complex. In the CSCE, each member is influenced by a simplex made from all other members of the current sub-complex. The CSCE barrows three actions of NM, i.e. reflection, contraction and expansion, and applied them on each member to find a better candidate than the current one. The efficacy of the proposed algorithm is first tested on six benchmark problems. After achieving satisfactory performance on the test problems, it is applied to parameter identification problems and the obtained results are compared with some other algorithms reported in the literature. Numerical results and non-parametric analysis show that the proposed algorithm is very effective and robust since it produces similar and promising results over repeated runs.

中文翻译：

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用于参数识别的企业洗牌复杂进化

本文提出了一种新版本的混洗复杂进化 (SCE) 算法，用于解决参数识别问题。SCE 将种群划分为多个称为复合体的并行子集，然后使用 Nelder-Mead (NM) 单纯形搜索方法通过进化过程改进每个子复合体。该算法仅将其进化过程应用于每个子复合体的最差成员，而其他成员的角色则不起作用。因此，搜索动作的数量和种类在 SCE 的进化过程中是有限的。目前的研究重点是通过提出企业分包商 (CSCE) 来克服这一缺点。该算法为子复合体的所有成员提供了进化的可能性。在 CSCE 中，每个成员都受到由当前子复合体的所有其他成员组成的单纯体的影响。CSCE 将 NM 的三个动作，即反射、收缩和扩展，应用到每个成员上，以找到比当前更好的候选者。首先在六个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，将其应用于参数识别问题，并将获得的结果与文献中报道的其他一些算法进行比较。数值结果和非参数分析表明，所提出的算法非常有效和稳健，因为它在重复运行时产生了相似且有希望的结果。在测试问题上取得令人满意的性能后，将其应用于参数识别问题，并将获得的结果与文献中报道的其他一些算法进行比较。数值结果和非参数分析表明，所提出的算法非常有效和稳健，因为它在重复运行时产生了相似且有希望的结果。在测试问题上取得令人满意的性能后，将其应用于参数识别问题，并将获得的结果与文献中报道的其他一些算法进行比较。数值结果和非参数分析表明，所提出的算法非常有效和稳健，因为它在重复运行时产生了相似且有希望的结果。