多阶段参数调整,增强优化设计的元启发式方法

IF 2.3 3区 工程技术 Q2 MECHANICS
Ali Kaveh, Amir Eskandari
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引用次数: 0

摘要

优化一直是科学和工程领域关注的焦点,许多元启发式算法已被开发并应用于各种问题。然而,这些算法往往需要调整参数才能达到合适的性能。本文提出了一种提高元启发式算法性能的新框架,称为多阶段参数调整(MSPA),它将元启发式算法、高效采样方法和机器学习融为一体。这里使用的采样方法被称为极限拉丁超立方采样法(XLHS),用于将参数空间划分为概率相等的子空间,确保因变量的连续性而获得更好的覆盖率。然后,利用选定的基准问题,针对不同数量的变量,通过初级优化器对这些参数进行改进。由此产生的数据被用来训练人工神经网络(ANN)。调整后的元启发式算法随后用于结构优化。在这方面,人工神经网络的输入数据包括每个子空间的下限和上限的平均值以及变量的数量,而输出数据则是使用初级优化器获得的优化值,无需对参数进行大量调整。为了评估拟议框架与原始版本和文献中其他一些算法相比的效率,对粒子群优化的参数进行了调整,并针对一些数学基准、两个工程问题和两个桁架结构优化问题进行了测试。结果表明,所提出的框架能有效提高元搜索算法的性能,特别是在桁架结构优化设计方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-stage parameter adjustment to enhance metaheuristics for optimal design

Multi-stage parameter adjustment to enhance metaheuristics for optimal design

Optimization has been a field of interest in science and engineering and many metaheuristic algorithms have been developed and applied to various problems. These, however, often require parameter adjustments to achieve a suitable performance. This paper proposes a new framework to improve the performance of metaheuristics, termed Multi-Stage Parameter Adjustment (MSPA), which integrates Metaheuristics, an efficient sampling approach, and Machine Learning. The sampling method utilized here known as Extreme Latin Hypercube Sampling (XLHS) is used to divide parameter spaces into equally probable subspaces, ensuring better coverage due to the continuous nature of variables. These parameters are then improved through a primary optimizer for different numbers of variables using a selected benchmark problem. The resultant data are utilized to train an artificial neural network (ANN). The adjusted metaheuristic algorithm is subsequently employed for structural optimization. In this respect, the input data for the ANN comprise the average of the lower and upper bounds of each subspace and the number of variables, while output data are the optimized values obtained using the Primary Optimizer, which does not require extensive parameter adjustments. To evaluate the efficiency of the proposed framework in comparison with the original version and some other algorithms in the literature, the parameters of Particle Swarm Optimization, chosen for its widespread applicability, are adjusted and tested against some mathematical benchmarks, two engineering, and two truss structural optimization problems. Results demonstrate the efficacy of the presented framework in enhancing the performance of metaheuristic algorithms, particularly in the optimal design of truss structures.

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来源期刊
Acta Mechanica
Acta Mechanica 物理-力学
CiteScore
4.30
自引率
14.80%
发文量
292
审稿时长
6.9 months
期刊介绍: Since 1965, the international journal Acta Mechanica has been among the leading journals in the field of theoretical and applied mechanics. In addition to the classical fields such as elasticity, plasticity, vibrations, rigid body dynamics, hydrodynamics, and gasdynamics, it also gives special attention to recently developed areas such as non-Newtonian fluid dynamics, micro/nano mechanics, smart materials and structures, and issues at the interface of mechanics and materials. The journal further publishes papers in such related fields as rheology, thermodynamics, and electromagnetic interactions with fluids and solids. In addition, articles in applied mathematics dealing with significant mechanics problems are also welcome.
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