Application of greedy algorithm and multi scale optimization algorithm in dynamic grid computing

Aiming at the high computational complexity of the traditional radial basis function (RBF), which is difficult to be applied effectively in large-scale computation, a method using greedy algorithm and multi-scale optimization algorithm is proposed to optimize the RBF. Based on RBF calculation method, this paper explores the application of greedy algorithm and multi-scale optimization algorithm in dynamic grid computing. Greedy algorithm improves computing efficiency and accuracy by selecting the best node, while multi-scale optimization algorithm improves computing speed and accuracy by dividing the grid into different scales. The effectiveness of greedy algorithm and multi-scale optimization algorithm to improve the efficiency and accuracy of dynamic grid computing is proved by an example.