Research And Application Of Swarm Intelligence Algorithm

The characteristics of the practical engineering problems, such as complexity, constraint, nonlinearity and difficulty of modeling etc, ask for the higher effectiveness of optimization and computation technology, Therefore, it is important to find a new type of intelligent optimization method. Swarm intelligence, as a kind of intelligence computation, which is attractive for more and more researchers.This dissertation focuses on two algorithms of Swarm Intelligence: Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO), on the basis of systematical research of PSO algorithm and its modification, based on the phenomena that a lot of particles crowded around the best position and many particles repeated an ineffective search in search later period,Proposes an improved algorithm mode, which is called Particle Swarm Optimization algorithm based on Self-adaptive Search Area (SSAPSO), it has better optimization performance than PSO and avoids effectively the precocious convergence problem. Secondly, after analyzing the disadvantages of the classical K-means clustering algorithm, combines the core idea of k-means clustering method with PSO algorithm and proposes a clustering algorithm based on PSO algorithm, it used the global optimization of PSO algorithm to make up the shortage of clustering method and enhances convergent rate.Finally, combines the clustering algorithm based on PSO algorithm with Ant Colony Optimization algorithm and apply it to Traveling Salesman Problem (TSP). Divides the large-scale TSP into much small scale TSP, and then carries on the parallel processing.Solved the problem of slowly convergent rate of Ant Colony Optimization algorithm. The application effect is comparative ideal.