A hybrid algorithm is a methodology that combines different techniques, such as genetic algorithms and Swarm Optimization Algorithms, or the integration of machine learning with traditional optimization algorithms. The goal of this integration is to take advantage of the advantages of each technology to speed up the search for optimal solutions and reduce the chances of falling into the local minimum (Local Minima). Stages of algorithm development 1. Problem analysis: Determine the nature of the problem and the criteria to be improved. 2. Choosing basic algorithms: combining multiple algorithms that integrate with each other to achieve the best performance. 3. Hybrid model design: Establish a mechanism of interaction between algorithms to ensure continuous improvement of results. 4. Model training and testing: Using experimental data to verify the efficiency of the algorithm compared to traditional methods. 5. Performance evaluation: Analyzing improvement results using specific metrics such as accuracy (accuracy), time taken (Execution time), and stability ratio (Robustness). Advantages of hybrid algorithms • Increase the speed of access to optimal solutions. • Improve the accuracy of results and reduce errors. • Ability to deal with multidimensional and complex problems. • Higher flexibility in its application to multiple fields such as computer vision, network planning, and industrial process improvement. Practical applications Hybrid algorithms have been applied in various fields, such as: • Improve the performance of intelligent robots in dynamic environments. • Development of high-precision predictive models in medicine and economics. • Improving artificial neural networks to reduce errors and increase learning speed. • Solving scheduling and resource problems in the industry. Challenges • The need for significant computer resources when dealing with large and complex problems. • Difficulty choosing the optimal combination of algorithms to balance speed and accuracy. • Ensure generalization to new and unfamiliar data. Conclusion The development of an AI-based hybrid optimization algorithm is an advanced step towards building more efficient and accurate intelligent systems. These algorithms contribute to improving performance in a wide range of applied fields, which enhances the position of artificial intelligence as a key tool for solving complex problems and achieving excellence in scientific research and practical applications