The Improved Light Ray Optimization Based On Runge-Kutta

In order to solve many practical problems in production and life,while keeping the interests of the maximum and the minimum cost, mathematicians and engineers abstract the similar problems that they encounted and summarize them into the global optimal value problem. Intelligent optimization problem, which is spired by some physical phenomena or ecological process is proposed by simulating it. The advantages of these algorithms are simple, easy to use, much stabler and suitable for the parallel computing, especially suits for dealing with complex problems that traditional methods can't solve. Therefore, the development of intelligent optimization algorithm have been paid more and more attention to by experts and scholars.Light Ray Optimization is a new intelligent optimization algorithm that based on Fermat's principle proposed by Professor Shen Jihong. It is a complete simulation of the propagation path of true light in different media. It is divides the feasible region into a number of small rectangles, then assumes the medium is homogeneousmedium in the same rectangular grid which is filled with the same refractive index of the medium each rectangular grid, and the path of finding the optimal solution is conceived as the actual light.In the optimization process,the adjustable parameters are few and the iteration rule is simple.In this paper, the two main jobs are as follows:Firstly, based on the existing literature, according to the optimization mechanism of Light Ray Optimization tracing algorithm, we applied the Runge-Kutta solution and implicit Euler solution to the equations of light rays and analyzed the stability of numerical soulution. In accordance with the corresponding equation which was combined the orm of the numerical algorithm with the equations of light rays, we carried out some numerical experiments by MATLAB.Secondly, after analyzing the results of numerical experiments, we improved the process by the method of thin and fine mesh. According to the improved results, we obtained the method which could make the improved Light Ray Optimization algorithm find the optimal solution at a faster speed, meanwhile it shortened the research time. Then,it was certain that the improved algorithm is feasible.