The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. حسب Yuanchen Zhao (12905580)

الموضوعات:

Scheduling time of five algorithms. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

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

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

CEC2017 basic functions. حسب Tengfei Ma (597633)

الموضوعات:

Procedure of the DCT-based EIT algorithm. حسب Rongqing Chen (249906)

الموضوعات:

Completion times for different algorithms. حسب 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. حسب 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). …"

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy حسب Yujin Wu (2901128)

الموضوعات:

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy حسب Yujin Wu (2901128)

الموضوعات:

CEC2017 test function test results. حسب Tengfei Ma (597633)

الموضوعات:

Simulation settings of rMAPPO algorithm. حسب 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). …"

Construction of the PRG score index using integrated machine learning algorithms. حسب Xiangxin Wu (22320058)

الموضوعات:

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

الموضوعات:

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

الموضوعات:

Fitness function parameters. حسب Ankita Sharma (3183054)

Curves of sigmoid functions used in our proposed algorithm. حسب Binh Thanh Dang (12368831)

"…<p>Curves of sigmoid functions used in our proposed algorithm.</p>…"

Power consumption for SHA256 software algorithm. حسب Maytham S. Jabor (15853046)

الموضوعات:

Benchmark functions T-test for the proposed and compared algorithms. حسب El-Sayed M. El-kenawy (14581088)