Matlab code of a version using numerical optimization for Q. by Hiroko Kato Solvang (5718250)

R code for a version using numerical optimization for Q. by Hiroko Kato Solvang (5718250)

Algorithm optimization process. by Zhihao Li (359073)

“…Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (<i>SOC</i>) as the optimization objectives. …”

Optimization results of different algorithms. by Haijian Wang (746739)

“…Finally, the effectiveness of the optimization results was verified using simulation experiments. …”

Illustration of the continuation algorithm used for optimization in the inversion method. by Karoline Horgmo Jæger (6785450)

Optimization flow chart of the AO algorithm. by Haijian Wang (746739)

“…Finally, the effectiveness of the optimization results was verified using simulation experiments. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Iteration curves of different algorithms. by Tengfei Ma (597633)

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

Flowchart of the operation of TLK-DBO algorithm. by Zhihao Li (359073)

“…Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (<i>SOC</i>) as the optimization objectives. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Algorithms runtime comparison. by Meilin Zhu (688698)

“…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …”

Convergence factor <i>α</i> change graph. by ZeSheng Lin (20501356)

Solution results of different algorithms. by Meilin Zhu (688698)

“…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …”

Thermoenergetic optimization of a school building: discussion of the performance of four multiobjective evolutionary algorithms by Thalita dos Santos Maciel (12656181)

“…This study discusses the performance of four multi-objective optimization algorithms applied to a standard project on early childhood education, developed by the Brazilian government program Proinfância, implemented in bioclimatic zone 2. …”

Table2_A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering.DOCX by Xing Huang (129439)

Table1_A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering.DOCX by Xing Huang (129439)

N2O mass fraction in Fig 15-turbulence changed. by Feng Gao (3548)

S1 Data - by Ningyan Chen (14675833)

“…Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. …”