Hybrid particle swarm optimizer with line search
Yu Liu, Zheng Kun Qin, Zhewen Shi · 2005
Particle swarm optimization, a new good swarm intelligence paradigm, has been successfully applied to many non-linear optimization problems. In a swarm each particle adjusts it's flying toward a promising area depending on cooperative interaction with others. The cooperative interaction of particles provides effective ways to determine the right flying direction for every particle, which is the key reason for the success of PSO. However, previous PSO algorithms are not good at choosing the step-size along the promising direction. In this paper a line search method is employed to enhance particle swarm optimizer so that the step size is chosen rationally. The experimental results show that PSO with line search method has a potential to achieve better solutions.