Simulation parameters for the QL, ABSQL and DSRABSQL algorithms. by Jin Zhang (53297)

Test results of different algorithms. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

Satisfaction of university teachers and students with various face recognition algorithms. by Yan Liu (25061)

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.…”

Total ranking of algorithms in different dimensions. by Wei Liu (20030)

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.…”

First-order lag averaging filtering algorithm. by Shaohua Fu (10573209)

“…The system improves the algorithm of the data collected by the sensor, and proposes an improved first-order lag average filtering algorithm. …”

Algorithm for extending the audiogram with loudness growth. by Lars Lambriks (14591615)

Computational time of each algorithm—Part I. by Xuan Chen (158617)

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Computational time of each algorithm—Part II. by Xuan Chen (158617)

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Shows the LL component of output from the wavelet filter function for normal, benign, and malignant lung cancer samples. by Tehnan I. A. Mohamed (16846175)

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

“…The algorithm employs recurrent neural networks to capture and process historical information. …”

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

“…The algorithm employs recurrent neural networks to capture and process historical information. …”

Step by step comparison of enrichment algorithms. by Nicholas Markarian (18251668)

Optimization results of the 8 different algorithms including ADA on 12 benchmark functions. by Bitanu Chatterjee (15295261)

Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

“…The mathematical model was transformed into a fitness function and a solution was provided with the Tabu Search Algorithm and Simulated Annealing. …”

APSO–WOA–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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

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