Compare algorithm parameter settings. حسب Yuqi Xiong (12343771)

الموضوعات:

Results of unimodal benchmark functions (F1–F7). حسب Xiaofeng Qu (577153)

الموضوعات:

A hybrid algorithm based on improved threshold function and wavelet transform. حسب Bingbing Li (461702)

الموضوعات:

Comparison algorithm control parameters. حسب Yong Gao (29929)

الموضوعات:

3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. حسب Giacomo Janson (8138517)

The SSIM for the different algorithms. حسب Bingbing Li (461702)

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

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

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Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" حسب Saeed Parsa (13893726)

"…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…"

Comparison of functions. حسب Shufen Niu (4423717)

Results of test functions (F12–15) with fixed dimensions. حسب Dingming Yang (8505990)

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Composite benchmark functions. حسب Qi Song (121209)

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Unimodal benchmark functions. حسب Qi Song (121209)

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3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. حسب Giacomo Janson (8138517)

EALM algorithm. حسب Weibo Wang (782424)

Route for bays29 output by ABSQL algorithm. حسب Jin Zhang (53297)

"…The experimental results show that DSRABSQL significantly outperforms the other algorithms. …"

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

Ave and Std of different algorithms. حسب Yanzhao Gu (21192659)

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Parameter settings for metaheuristic algorithm. حسب Yanzhao Gu (21192659)

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Scheduling time of five algorithms. 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. …"