An Improved Chaotic Particle Swarm Optimization and Its Application in Investment

Particle swarm optimization is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. But itpsilas easy to be trapped into local optimum. Based on the chaos theory, the random function is introduced to Tent map, and the improved Tent map is introduced to PSO. Update the velocity and position of the particle by the improved Tent map instead of the random parameters. Eliminate the particle whose position is the farthest to the optimal solution after iterating certain steps, and reestablish the position of new particle according to average value of the positions of all the particles to search again. The algorithm has faster convergence and better global optimization capability. The improved Tent PSO is applied to the investment optimization, and the result of simulation shows better optimization function.