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Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems
Applied Soft Computing ( IF 7.2 ) Pub Date : 2020-11-24 , DOI: 10.1016/j.asoc.2020.106934
Huachao Dong , Peng Wang , Xinkai Yu , Baowei Song

In this work, a surrogate-assisted teaching-learning-based optimization algorithm is presented for high-dimensional and computationally expensive black-box optimization problems. In the presented method, a two-phase searching framework is proposed to realize the exploitation of surrogates and the metaheuristic exploration. Specifically, radial basis functions are used to build the dynamically updated surrogate models. Moreover, a surrogate-assisted knowledge mining strategy is proposed to sufficiently collect valuable information in each cycle. In this strategy, multiple groups of promising points are selected from the expensive sample set to construct the subspaces and local surrogate models, from which the most potential points are captured for the subsequent updates and optimization. At the same time, a population composed of the present best expensive samples carries out the teaching/learning-based search, accelerating local convergence and promoting global exploration. In the surrogate-assisted teaching phase, both of individual and mean behaviors are considered to make learners go close to the teacher efficiently. In the surrogate-assisted learning phase, a more extensive search range is defined to improve sampling diversity. The cooperation of the surrogate-assisted knowledge mining and the modified teaching-learning-based exploration makes the new method have excellent performance on 21 benchmark cases with 30–100 design variables. Furthermore, this method is used for the shape optimization of a blended-wing-body underwater glider, and gets impressive results.



中文翻译:

基于代理辅助的基于教学学习的优化解决高维和计算量大的问题

在这项工作中,针对高维和计算量大的黑箱优化问题,提出了一种基于代理辅助的基于教学学习的优化算法。在该方法中,提出了一个两阶段搜索框架,以实现代理人的开发和元启发式探索。具体来说,径向基函数用于构建动态更新的替代模型。此外,提出了一种代理辅助的知识挖掘策略,以在每个周期中充分收集有价值的信息。在这种策略中,从昂贵的样本集中选择多组有希望的点,以构建子空间和局部代理模型,从中捕获最有潜力的点,用于后续更新和优化。同时,由目前最昂贵的样本组成的人口进行基于教学/学习的搜索,加快了本地融合并促进了全球探索。在代理辅助教学阶段,个人行为和平均行为都被认为可以使学习者有效地接近老师。在替代辅助学习阶段,定义了更广泛的搜索范围以改善采样多样性。代理辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。加快地方融合,促进全球勘探。在代理辅助教学阶段,个人行为和平均行为都被认为可以使学习者有效地接近老师。在替代辅助学习阶段,定义了更广泛的搜索范围以改善采样多样性。替代辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。加快地方融合,促进全球勘探。在代理辅助教学阶段,个人行为和平均行为都被认为可以使学习者有效地接近老师。在替代辅助学习阶段,定义了更广泛的搜索范围以改善采样多样性。替代辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。个人行为和卑鄙行为都被认为可以使学习者有效地接近老师。在替代辅助学习阶段,定义了更广泛的搜索范围以改善采样多样性。代理辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。个人行为和卑鄙行为都被认为可以使学习者有效地接近老师。在替代辅助学习阶段,定义了更广泛的搜索范围以改善采样多样性。替代辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。代理辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。替代辅助知识挖掘与改进的基于教学的探索相结合,使该新方法在21个具有30-100个设计变量的基准案例中具有出色的性能。此外,该方法还用于混合翼体水下滑翔机的形状优化,并获得了令人印象深刻的结果。

更新日期:2020-11-25
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