数学优化
梯度法
各向同性
水准点(测量)
分类
计算机科学
拓扑优化
帕累托原理
启发式
遗传算法
梯度下降
拓扑(电路)
应用数学
算法
数学
有限元法
人工神经网络
地质学
物理
人工智能
组合数学
热力学
量子力学
大地测量学
作者
Vivek T. Ramamoorthy,Ender Özcan,Andrew J. Parkes,Luc Jaouen,François‐Xavier Bécot
摘要
When designing passive sound-attenuation structures, one of the challenging problems that arise is optimally distributing acoustic porous materials within a design region so as to maximise sound absorption while minimising material usage. To identify efficient optimisation strategies for this multi-objective problem, several gradient, non-gradient, and hybrid topology optimisation strategies are compared. For gradient approaches, the solid-isotropic-material-with-penalisation method and a gradient-based constructive heuristic are considered. For gradient-free approaches, hill climbing with a weighted-sum scalarisation and a non-dominated sorting genetic algorithm-II are considered. Optimisation trials are conducted on seven benchmark problems involving rectangular design domains in impedance tubes subject to normal-incidence sound loads. The results indicate that while gradient methods can provide quick convergence with high-quality solutions, often gradient-free strategies are able to find improvements in specific regions of the Pareto front. Two hybrid approaches are proposed, combining a gradient method for initiation and a non-gradient method for local improvements. An effective Pareto-slope-based weighted-sum hill climbing is introduced for local improvement. Results reveal that for a given computational budget, the hybrid methods can consistently outperform the parent gradient or non-gradient method.
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