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

AUC scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

الموضوعات:

Recall scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

الموضوعات:

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

الموضوعات:

F1-scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

الموضوعات:

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. 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)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. 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. …"

Fitness function parameters. حسب Ankita Sharma (3183054)

Scheduling time of five algorithms. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. 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. …"

Convergence speed of five algorithms. حسب Huichao Guo (14515171)

"…In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. 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. …"

Results of searching performance of different algorithm models on the Sphere function and Griewank function. حسب Rui Zhang (13940)

"…<p>Results of searching performance of different algorithm models on the Sphere function and Griewank function.…"

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. حسب Rui Zhang (13940)

The Precision-Recall curve of both the models. حسب Aastha Vatsyayan (9981916)

الموضوعات:

Comparison curve of objective function (). حسب Wanjun Zhang (496858)

Benchmark functions T-test for the proposed and compared algorithms. حسب El-Sayed M. El-kenawy (14581088)

Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions. حسب El-Sayed M. El-kenawy (14581088)

Comparison curve of objective function (J). حسب Wanjun Zhang (496858)

Procedure of the DCT-based EIT algorithm. حسب Rongqing Chen (249906)

الموضوعات:

Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions. حسب El-Sayed M. El-kenawy (14581088)

Comparison of the performance of both models against 13 other models on the independent test dataset. حسب Aastha Vatsyayan (9981916)

الموضوعات: