Machine learning algorithm parameters. by Namal Rathnayake (15361124)

“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

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

The structure of the proposed algorithm. by Mingbao Yang (14008799)

AIF and VOF derived features used to feed the machine learning algorithms. by Ezequiel de la Rosa (11361108)

Fitness function curve at weight factor 0.3. by Dhulfiqar Hakeem Dhayef (17729684)

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The proposed feature extraction algorithm. by Ali Ahmed (2567584)

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Fitness function parameters. by Ankita Sharma (3183054)

Run times of two algorithms. by Gourab Saha (8987405)

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Average running time of different algorithms. by Wei Liu (20030)

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

“…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. …”

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Comparison of key evaluation indicators for recommendation algorithms. by Mingbao Yang (14008799)

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). …”

Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Benchmark functions. by Mingjun Li (178925)

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The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

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The IALO algorithm solution flowchart. by Wenjing Wang (181404)

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

“…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. …”