Application Research Of Ant Colony Algorithm And Wavelet Network In Complexity Science

Complexity science is a new developing interdisciplinary in the 21st Century. Exploring complexity is becoming the frontier of modern science. This paper mainly focuses on the improved algorithm of ant colony algorithm and wavelet network,explores the application in complexity science correspondingly, which includes the theory analysis of ant colony algorithm based on swarm intelligence, the parameters optimization setting of ant colony algorithm, the improved ant colony algorithm based on genetic algorithm and its application of plant disease spot detection,the initialization setting for the parameters of ant colony algorithm,the structural optimization of wavelet network based on ant colony algorithm and the model construction of decision level information fusion algorithm based on improved wavelet network.The innovations of the thesis are embodied in four aspects as follows:(1)The principle of parameters setting for ant colony algorithm based on meta-heuristic frame is proposed and the orthogonal experiment method is designed to optimize the parameters setting as well. The correlation analysis of key parameters can be used by statistical method, the experiment times be reduced by orthogonal design, the optimal settings be obtained, the subjective problem be overcome and the efficiency of parameter selection be improved correspondingly.(2)Adaptive genetic ant colony algorithm is proposed and the detection model of plant disease spot is designed by the global combination optimization method based on adaptive genetic ant colony algorithm correspondingly. In the adaptive genetic ant colony algorithm, the submodule of genetic algorithm and ant colony algorithm are improved respectively. As for the submodule of genetic algorithm, the adaptive selection algorithm of crossover probability and adaptive mutation probability is designed and the selection principle of fitness function is discussed, too. For the submodule of ant colony algorithm, the dynamic pheromone updating mechanism based on adaptive pheromone volatilization factor is designed,which can realize the final improvement of genetic ant colony algorithm. According to the principle of global combination optimization, the plant disease plot detection based on improved genetic ant colony algorithm is finally realized. (3)Improved wavelet network based on ant colony algorithm is proposed.According to the global optimization capacity of ant colony algorithm,the learning algorithm of wavelet network based on ant colony algorithm is constructed to realize the optimization setting of weight, threshold, scale factor and translation factor.(4)The model construction of decision level information fusion algorithm based on improved wavelet network is proposed. On the basis of data missing mechanism and its processing method, a series of wavelet network with parallel network structure are combined to construct the feature level information fusion model.Finally, the model construction of decision level information fusion algorithm is designed by wavelet network and evidence theory.Simulation results verified the correctness of the above improved algorithm and information fusion model.