Greedy algorithm in solving the problem, it is from the initial stage, in each stage is to make a local optimal greedy choice. Each time the greedy choice transforms the original problem into a sub problem of th
The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including ...
Metaheuristic algorithm simulates the population behavior in nature for mathematical modeling, and has strong optimization ability. Its advantages in the field of optimization are obvious to all, especially in the field of parameter optimization, it is trusted by people. 2. Introduction to the ...
5.1.2 Barycentric Greedy Algorithm (BGA), A Sparse but Non-Adaptive Approach One of the main challenges of interpolation methods based on Wasserstein barycenters is the choice of the support of the vector of weights ωNn. As discussed above, relying on the set of n-nearest neighbors of a sa...
South Korea by applying algorithmic approaches to survey data. Specifically, it applies a decision tree classification algorithm, light gradient boosting machine (LightGBM), and SHapley Additive exPlanations (SHAP) to estimate the importance of the studied variables and to interpret and analyze the ...
In this case, greedy heuristics can be used to estimate the exact solution [42,85,200]. 4.2.3 Game theory Lately, there has been also a use of game theoretic approaches to deal with resource allocation problems. Through game theory, the task offloading problem can be introduced as a ...
In this paper we present the application of a recently proposed, general, algorithm for combinatorial optimization to the unbalanced minimum common string partition problem. The algorithm, which is labelled Construct, Merge, Solve & Adapt, works on sub-i
RFE is a greedy algorithm for finding the optimal feature subset. SVM model was used as the model of RFE in our study. Cost-sensitive learning SVM is good at high dimension data, making it popular for many ML practitioners. Furthermore, in the SVM model, by changing the weights of ...
To solve bankruptcy prediction tasks, we proposed an improved rime optimization technique (RMRIME). The proposed RMRIME algorithm first employs roulette wheel selection step, introducing random individuals into the position updating process to expand the search space and boost the RMRIME’s exploration...
Alcin, O.F.; Sengur, A.; Ghofrani, S.; Ince, M.C.: GA-SELM: Greedy algorithms for sparse extreme learning machine. Measurement 55, 126–132 (2014) Article Google Scholar Huang, G.B.; Zhu, Q.Y.; Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70...