Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approx...Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.展开更多
One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML...One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil Samples.Validation of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for comparison.Results showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.展开更多
社交网络链路预测旨在根据已知的网络信息预测未来的链接关系,在推荐系统和合著网络中具有重要作用.然而,现有链路预测算法往往忽视社交网络的多元演化特点,训练时间复杂度较高,限制其执行效率.针对上述问题,文中提出基于多演化特征的...社交网络链路预测旨在根据已知的网络信息预测未来的链接关系,在推荐系统和合著网络中具有重要作用.然而,现有链路预测算法往往忽视社交网络的多元演化特点,训练时间复杂度较高,限制其执行效率.针对上述问题,文中提出基于多演化特征的社交网络链路预测算法(Multi-evolutionary Features Based Link Prediction Algorithm for Social Network,MEF-LP).首先,设计一种简单高效的时间极限学习机模型,利用门控网络和极限学习机自编码器传递与聚合社交网络快照序列的时间信息.然后,构建多个深度极限学习机,对时间特征进行多角度映射,挖掘社交网络不同的演化特征,并最终融合成综合演化特征.最后,使用基于极限学习机的分类器完成链路预测.在6个真实社交网络上的实验表明,MEF-LP能合理学习社交网络的多演化特征,并获得较优的预测性能.展开更多
基金funded by Hanoi University of Civil Engineering(HUCE)in Project Code 35-2021/KHXD-TD.
文摘Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.
基金Ministry of Education and Training of Vietnam,Grant No.B2020-GHA-03the University of Transport and Communications,Hanoi,Vietnam.
文摘One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil Samples.Validation of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for comparison.Results showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.
文摘社交网络链路预测旨在根据已知的网络信息预测未来的链接关系,在推荐系统和合著网络中具有重要作用.然而,现有链路预测算法往往忽视社交网络的多元演化特点,训练时间复杂度较高,限制其执行效率.针对上述问题,文中提出基于多演化特征的社交网络链路预测算法(Multi-evolutionary Features Based Link Prediction Algorithm for Social Network,MEF-LP).首先,设计一种简单高效的时间极限学习机模型,利用门控网络和极限学习机自编码器传递与聚合社交网络快照序列的时间信息.然后,构建多个深度极限学习机,对时间特征进行多角度映射,挖掘社交网络不同的演化特征,并最终融合成综合演化特征.最后,使用基于极限学习机的分类器完成链路预测.在6个真实社交网络上的实验表明,MEF-LP能合理学习社交网络的多演化特征,并获得较优的预测性能.
