Rosenbrock Function Optimization Based on Improved Particle Swarm Optimization Algorithm
Shao Pen · 2013
Rosenbrock function optimization belongs to unconstrained optimization problems,and its global minimum value is located in the bottom of a smooth and narrow valley of the parabolic shape.It is very difficult to find the global minimum value because of little information the function optimization algorithm provided.According to the characteristics of the Rosenbrock function,this paper specifically proposed an improved particle swarm optimization(PSO)algorithm(PSO-R)which introduces trigonometric factor with periodic oscillations of trigonometric functions so that each particle gets strong oscillation to expand the search space of each particle and guide particles to close nearly the optimal value and avoid converging prematurely and local optimum,and find the global minimum of the Rosenbrock function.A large number of experimental results show that the algorithm has good performance of function optimization,and provides a new idea for optimization problems similar with the Rosenbrock function for some problems of special fields.