Universal Gravitation Optimization And It’s Application In QoS Routes

Universal gravitation optimization(UGO) algorithm which lodge in Newton’s universal gravitation is a new optimization algorithm which have been widely researched and get attention in recent years, The study found that, UGO algorithm is superior to traditional optimization algorithm in many respects and it has incomparable advantage, so UGO algorithm get a lot of researchers’ favour.UGO compared to traditional optimization algorithm, it has simple principle, programming easy to implement, adjustable parameter less and fast convergence rate. Since UGO’s portability is strong, it can be used in many engineering practice. At present, UGO’s research mainly focus on two aspects:research algorithm itself and its application. The algorithm itself researched mainly includes proof of convergence, verification of convergence speed and fusion research of the universal gravitation optimization algorithm and the other optimization algorithms.This article mainly includes three works. The first work is to structure the three kinds of gravitational optimization algorithm:(1) only consider gravity’s first largest point and gravity’s second largest point to the particle produce effective gravity;(2) only consider gravity’s first largest point and the nearest point to the particle produce effective gravity;(3) only consider gravity’s first largest point and a random point to the particle produce effective gravity. These three kinds of algorithm compared with each other, also compared with the particle swarm algorithm and the traditional gravity algorithm, the result shows that this article’s three kinds of gravity optimization algorithm’s performance has a certain enhancement, this part of contents is introduced in chapter3.The second work is the function test and the convergence analysis of the three kinds of gravitational optimization algorithm:(1) Test the three kinds of gravitational optimization algorithm with functions for their optima, compared the three kinds of algorithm with each other, also compared with particle swarm algorithm, get the relevant conclusion with experimental data.(2) Gravity optimization algorithm convergence is analyzed using random algorithm convergence analysis method. This part of contents is in chapter3, too.The third work is quality of service multicast routing research for path planning, path planning is the research hotspots for quality of service multicast routing, want to get a better multicast path, the researchers studied a lots of methods, such as the ant colony optimization method, the particle swarm method, etc. This paper mainly is to explore the use of gravity optimization algorithm to path planning for quality of service multicast routing, mainly based on gravity optimization algorithm to analyze work environment modeling of quality of service multicast routing, translation quality of service multicast routing problem into the path optimization problem, and give routing related path optimization algorithm. The gravity optimization algorithm is applied in to solve the path optimization. From the experimental results we know, gravity optimization algorithm can be basically realized path planning of quality of service multicast routing, but in its deep performance remains to be further verification. This part of contents is in chapter4.