Convergence speed of five algorithms. by Huichao Guo (14515171)

“…In addition, with the increase of pipeline complexity, NACFPSO can still maintain its advantages in convergence speed and scheduling time, especially in scheduling time, which further verifies the optimization effect of the algorithm in emergency management.…”

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Hyperparameter settings of the algorithm 1. by Jin Xu (31283)

“…Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). …”

General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function. by Alexander V. Spirov (11302085)

Subjects:

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

Subjects:

Boxplot of fitness in various algorithms. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

The confusion matrix of the different algorithms. by Qiushi Wang (417314)

Table_1_Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.DOCX by Mohammad Dashtkoohi (15417886)

One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

Algorithm parameters. by Guilin Yang (583364)

“…To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. …”

Mean training time of different algorithms. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Algorithm ranking under different dimensions. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Information of nodes and pipes of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of the NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The result of Wilcoxon signed-rand test. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”