Exploration Based on Global Iterative Search Algorithm Model

This study explores the role of single-objective optimization algorithms, grid methods, and global iterative search algorithms in solving various problems. These algorithms play a key role in the application of different fields and can find the optimal solution to meet the needs of the problem. Single-objective optimization algorithm is used to solve the optimization problem of single objective function, which is suitable for machine learning, scientific research and other fields. The grid method is a powerful optimization method for discrete problems, which is widely used in image processing and parameter adjustment. The global iterative search algorithm is designed to find the global optimal solution, which is suitable for multi-modal and high-dimensional problems, power system planning and machine learning model parameter optimization. The application of these algorithms provides an effective tool for all walks of life to optimize resource utilization, improve efficiency, improve the decision-making process, and have a profound impact on future technological development and problem solving. Their ability to handle complex and high-dimensional problems makes them indispensable tools for better results in areas such as scientific research, business decision-making and engineering design.