Single Objective Optimization ProblemsThis paper presents the basic introduction of the Optimization, Multi-Objective Optimization algorithms, Multi-Objective Optimization Problems and its application domain. It also emphasizes the differences and needs of the optimization on the basis of a single objective ...
This paper presents a methodology for the employment of the set of functional evaluations that is generated by the stochastic optimization algorithms, in order to obtain additional information, related to the objective functional sensitivity in the region nearby the solution point. Three types of ...
Corominas,A,Moreno,N.On the relations between optimal solutions for different types of minsum balanced JIT optimization problems. INFOR . 2003Corominas, A. and Moreno, N. (2003). On the relations between optimal solutions for different types of min-sum balanced JIT optimisation problems. INFOR,...
Teaching–learning based optimization (TLBO)c–Φ soilRetaining wallPseudo-static seismic forceRecently, many heuristic global optimizations successfully applied in various types of real life problems. The majority of these algorithms had not been applied to solve civil engineering problem, especially in ...
MATHEMATICAL optimizationANIMAL social behaviorPARTICLE swarm optimizationBIOLOGICAL evolutionMany optimization algorithms that imitate the social behaviour of animals and natural biological evolution have been proposed in the recently preceding years. These nature inspired algorithms known as evolutionary al...
4 Python 181657 43921 Python 26 All Algorithms implemented in Python 2024-06-24T22:00:50Z 5 AutoGPT 163381 43337 Python 64 AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters. 2024-...
A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions. - fabsig/KTBoost
However, the research about a comparison of different machine learning methods is rare; particularly, a comparison of the NN, Extreme Gradient Boosting (XGBoost3), and Light Gradient Boosting Machine (LightGBM4) lacks. A study about the latter two machine learning algorithms in petroleum engineering...
The optimization problem is solved using the spotted hyena optimizer (SHO) algorithm to determine the optimal size and location of capacitors, considering different loading levels and the two operation modes. In this study, a three-level load and various types of loads, including constant power, ...
of images of any size and overcoming the limitation of CNNs requiring fixed input image sizes. However, FCN is insensitive to image details and does not consider relationships between pixels. The emergence of FCN has prompted extensive research into image segmentation algorithms, resulting in ...