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)

Subjects:

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

Subjects:

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)

Subjects:

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

“…<p>We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. …”

Benchmark test function result. by HaoCheng Wang (6089567)

Fixed multimodal test functions. by HaoCheng Wang (6089567)

Main parameters of the 7 algorithms. by Xuan Chen (158617)

Subjects:

Behavioral results. by Sam Hall-McMaster (10343795)

Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

Choices on individual test tasks were not explained by model-free perseveration. by Sam Hall-McMaster (10343795)