Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Convergence curves obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

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Different algorithms in 23 benchmark functions to obtain partial boxplot. by Yuqi Xiong (12343771)

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Results obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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Convergence curves obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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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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Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. by Jiaxi Wang (545353)

The image of the Aekley function. by Zhengyu Xu (8550660)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. …”

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

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Standard functions to test. by Kemal Gökhan Nalbant (18858009)

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. …”

PSO algorithm flowchart. 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. …”

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Fuzzy membership function for yaw with . by Yasir Mehmood (744452)

Comparison of different algorithms. by Dawei Wang (471687)

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The benchmark functions. by Shuhui Yu (1535500)

Fuzzy membership function for the longitudinal velocity with s. by Yasir Mehmood (744452)