The Research On Parameter M Of Light Ray Optimization Algorithm

Nowadays,with the rapid development of science and technology,more and more complex scientific research and practical engineering problems have accompanied by the development of science and technology.The most important one inside is the optimization problem which is often encountered in the production practice.In solving this kind of problem usually exist high dimension,large amount of iteration,long time of iteration,complex process of solution,and so on,which Often make people's needs can not be met in the traditional way.To this end,a number of intelligent algorithms based on biological instinct and natural phenomena rised in response to the proper time and conditions.This kind of algorithm has the characteristic of bright,simple and practical,good robustness,and also can be used in parallel computing.With the result that a large number of mathematical workers and engineers will study the new intelligent optimization algorithm as a new perspective.In 2007,Professor Shen Jihong based on Fermat's principle proposed the light optimization algorithm as a new optimization algorithm for the first time.The characteristic of this algorithm is that can simulate the propagation path of real light in different media.In this paper,we mainly do the following three jobs:Firstly,analyzes the cause of original light optimization algorithm can not up to the global optimum effect for some non positive function,proposed the method of positive function,and calculate the ideal range of parameters.Secondly,improved the light optimization algorithm by the properties of the parameters of ideal value range,compare with function optimization path and image through numerical experiments on a series of functions.We can see the improved program has greatly strengthened in searching the global optimum from the results,and the application of light optimization algorithm from non negative function is extended to general function.Thirdly,proposed more efficient way to improve the function which exist derivative.Using the definition of gradient in the steepest descent method and the choice of direction,applied into the improved algorithm.And give a detailed description of the procedures and processes,verify the feasibility of the algorithm by numerical experiments.