Two Intelligent Optimization Algorithm And Its Convergence Analysis

In recent years, intelligent optimization algorithm, as a solution to the complex, strong constraints and other characteristics of the application of engineering technology, has been rapidly developed and enhanced. Compared with the traditional optimization algorithm, the difference is that intelligent optimization algorithms stem from the phenomenon of life for inspiration in nature, and thus efficient to solve many of the traditional optimization techniques can not solve practical problems, demonstrating its unique advantages. However, the theory of intelligent intelligent optimization algorithm is not a lot of research, especially research in terms of convergence, therefore, of the algorithm and its convergence for the optimization theory of a certain practical significance.This paper introduces the four intelligent optimization algorithms and the convergence of the status quo. Secondly, by the particle swarm optimization (PSO algorithm) inspired in the exchange operator, the improved optimization algorithm fireworks explosions and the algorithm of the two convergence is proved. Again, this goes also given search algorithm, and according to the knowledge of probability theory gives the convergence is proved. For both optimization algorithm, this paper have carried out a simulation experiment, and then with the particle swarm algorithm are compared, etc. The results show that both optimization algorithms have achieved good results.This algorithm is proposed for the test and the convergence analysis, comparing the complete algorithm is given. Tested and found that there is a big advantage of the two algorithms. Meanwhile, the algorithm still there are many areas for improvement, both from the theory and application have great practical significance.