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

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

Types of training algorithms. حسب 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... حسب Giacomo Janson (8138517)

CEC2014 functions. حسب Adel Saad Assiri (9962517)

الموضوعات:

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

The benchmark test functions. حسب Xuxu Zhong (10099054)

الموضوعات:

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

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

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

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

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

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

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

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

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

الموضوعات:

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

الموضوعات:

The pseudocode for the NAFPSO algorithm. حسب 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. حسب 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. حسب Yong Gao (29929)

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