Hybrid particle swarm optimizer with line search

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.

[1]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[3]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Yu Liu,et al.  Supervisor-student model in particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[6]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).