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)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Types of training algorithms. by Caixia Yan (6647072)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

CEC2014 functions. by Adel Saad Assiri (9962517)

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

The benchmark test functions. by Xuxu Zhong (10099054)

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The optimization result of 10 benchmark functions for different algorithms (D = 10). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 30). by Yu Li (183836)

The result of CEC2010 standard functions for different algorithms (D = 30). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 100). by Yu Li (183836)

The convergence curves of the function in different dimensions. by Yu Li (183836)

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

The block diagram of the ASO algorithm implementation for inverse modeling. by Ahmad Al-Talabi (21387479)

The parameters of the algorithms. by Yu Li (183836)

Details of the metaheuristic algorithms. by Yanzhao Gu (21192659)

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Algorithm of genetic workup. by Idoia Martinez de Lapiscina (16528261)

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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. …”

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. …”

Comparison results of dynamic shift functions. by Yong Gao (29929)

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