协同模型与遗传算法的集成

提出协同模型中蚁群算法和遗传算法集成的方案，用于生成纹理分类的模板。介绍了蚁群算法的原理和集成方案的实施步骤，以及在搜索最优解过程中蚁群移动方向的决策方法。为了验证集成方案的正确性，对航空影像的居民地、灌木、水田、山地、旱地5种目标进行了识别试验，并与仅使用遗传算法的结果进行了比较。试验结果表明，该集成方案的识别效果有明显的提高。

The Intergation of the Cooperation Model and Genetic Algorithms

In recent years genetic algorithms have been applied to solve various problems of combinatorial optimization such as assignment problem,travelling salesman problem,etc.In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper_spectrum remote sensing data.Genetic algorithm is an adaptive procedure that searches for good solutions by using a collection of search points known as a population in FK(W20?40ZQ] order to maximize some desirable criteria.It can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.Ant colony algorithm was introduced by M.Dorigo and A.Colorni in 1951.It is a natural algorithm based on behaviour of ants in establishing paths from their colony to feeding sources and back.It has the following characteristics : (1) It is a natural algorithm since it is based on behaviour of ants .(2) It is parallel.In fact it concerns a population of agents moving simultaneously,independently and without a supervisor.It is cooperative since each agent chooses a path on the basis of the information (pheromone trails) laid by the other agents which have previously selected the same path.This cooperative behaviour is also autocatalytic,i.e.,it provides a positive feedback,since the probability of an agent choosing a path increases with the number of agents that previously chose that path.On the basis of the advantages of ant colony algorithm,this paper proposes the method to integrate genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.Experimental results on texture recognition show that the integration method presented in the paper is more effective than the method only using genetic algorithms.