用于解决相平衡问题和半经验模型参数估计的混沌 Aquila 优化算法

IF 4.9 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Oguz Emrah Turgut, Mert Sinan Turgut, Erhan Kırtepe
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引用次数: 0

摘要

本研究旨在通过采用混沌序列而不是使用均匀生成的高斯随机数来提高新出现的Aquila优化算法的优化性能。这项研究在 Aquila 优化器框架下采用了 25 种不同的混沌映射。它考虑了在由单模态和多模态问题组成的多维测试函数上进行性能评估的十种最佳混沌变体,这些问题在过去的文献中还没有研究过。结果发现,池田混沌图增强型 Aquila 优化算法的预测结果最好,在大多数情况下都处于领先地位。为了检验这种混沌变体在实际优化问题上的有效性,研究人员在两个受限工程设计问题上使用了该变体,并验证了其有效性。最后,利用所提出的方法求解了相平衡和半经验参数估计问题,并将各自的求解结果与最先进的优化器求解结果进行了比较。结果表明,CH01 能够成功应对参数估计和相平衡问题的限制性非线性和非凸性,与性能基准测试过程中使用的其他算法相比,CH01 能够产生不超过 0.05 的最小预测误差值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Chaotic Aquila Optimization Algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

Chaotic Aquila Optimization Algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 different chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the effectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its effectiveness has been verified. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.

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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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