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Surrogate-based optimization with adaptive parallel infill strategy enhanced by inaccurate multi-objective search
Engineering Optimization ( IF 2.7 ) Pub Date : 2021-06-17 , DOI: 10.1080/0305215x.2021.1928109
Wenjie Wang 1 , Zeping Wu 1 , Donghui Wang 1 , Pengyu Wang 1 , Weihua Zhang 1 , Patrick N. Okolo 2, 3 , Gareth J. Bennett 2
Affiliation  

In recent decades, surrogate-based optimization (SBO) has been developed to replace costly models with cheap surrogates to improve efficiency. In this article, an adaptive parallel infill strategy is proposed to balance exploration and exploitation over the design space during the optimization process of SBO. Within this method, an inaccurate search strategy is adopted to optimize the surrogate models, thereby helping to locate the exploitation point. An elite archive is exploited to store superior sampling points for batch sampling, while a customized batch size determination strategy is introduced. The proposed SBO method with its adaptive parallel sampling strategy is tested on six unconstrained and five constrained analytical cases with the optimization results compared to state-of-the-art optimization algorithms. The optimization of a 582-bar tower truss system is also performed and utilized to verify the proposed SBO method. The proposed SBO with the adaptive parallel sampling strategy shows excellent performance and better stability.



中文翻译:

通过不准确的多目标搜索增强自适应并行填充策略的基于代理的优化

近几十年来,已经开发了基于代理的优化 (SBO),以用廉价的代理代替昂贵的模型,以提高效率。在本文中,提出了一种自适应并行填充策略,以平衡 SBO 优化过程中对设计空间的探索和利用。在该方法中,采用不准确的搜索策略来优化代理模型,从而有助于定位利用点。利用精英档案存储批量采样的优质采样点,同时引入了定制的批量大小确定策略。所提出的 SBO 方法及其自适应并行采样策略在六个无约束和五个约束分析案例上进行了测试,优化结果与最先进的优化算法进行了比较。还对 582 杆塔桁架系统进行了优化,并用于验证所提出的 SBO 方法。所提出的具有自适应并行采样策略的 SBO 表现出优异的性能和更好的稳定性。

更新日期:2021-06-17
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