Changes in the target objective function for different confidence levels. by Yangjun Ren (17691912)

Location schemes and objective function values for different values of the confidence level. by Yangjun Ren (17691912)

Improved NSGA-II algorithm flowchart. by Ying Wang (11406)

“…To achieve the optimal composition scheme of shared manufacturing services, the Criteria Importance Though Intercrieria Correlation (CRITIC) is used to determine the weights of the indicators, and the improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II) is employed to solve the multi-objective optimization problem. …”

Solving the three-objectives numerical problem from [36] using a generalized version of the ROSE algorithm for three-objective problems: (a) The Pareto front representation corresp... by Ihab Hashem (21156806)

Flowchart indicating steps of the object detection part of algorithm based on the computer vision approach. by Maria Sokolova (3388097)

Comparing ROSE and the NBI algorithms with varying sizes of the Pareto set/levels of recursion of the ROSE algorithm when applied to the BIOBJ problem: (a) 5 points/two recursion l... by Ihab Hashem (21156806)

Genetic algorithm flowchart. by Junjie He (700642)

Subjects:

Heat map of multi-objective optimization results. by Wang Xiang (5435141)

“…After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. …”

Comparison of performance metrics of three algorithms. by Junjie He (700642)

Subjects:

Evolution of objective function. by Serdar Ekinci (15927455)

Objective convergence. by Dongyan Wang (195482)

Comparison of site selection results of three algorithms. by Junjie He (700642)

Subjects:

Means plots of genetic algorithm calibration parameters. by Junjie He (700642)

Subjects:

Objective function values and CPU times of the considered solution approaches. by Yangjun Ren (17691912)

Objective function values and CPU times of the considered solution approaches. by Yangjun Ren (17691912)

Pareto front representations obtained by solving the BIOBJ problem using the ROSE algorithm and the NBI algorithm, as well as a visualization of the solution process of the ROSE al... by Ihab Hashem (21156806)

Comparison analysis of the results before and after optimization. by Junjie He (700642)

Subjects:

Distribution of demand points by level. by Junjie He (700642)

Subjects:

Comparison of algorithms’ time complexity by varying the number of users. by Diego Gómez-Zará (14094047)

Subjects:

Results of the PSNR measure for all algorithms. by Jiaqi Ma (773451)

“…Aiming at the drawbacks of premature convergence of existing optimization algorithms for high-bit depth image segmentation, this paper presents a pyramid particle swarm optimization based on complementary inertia weights (CIWP-PSO), and the Kapur entropy is employed as the optimization objective. …”