Comprehensive learning particle swarm optimizer with guidance vector selection

In this paper, comprehensive learning particle swarm optimizer (CLPSO) is integrated with guidance vector selection. To update a particle´s velocity and position, several candidate guidance positions are constructed based on all particles´ best positions. Then the candidate guidance vector with the best fitness is selected to guide the particle. Simulation study is performed on CEC 2005 benchmark problems and the results show that the CLPSO with guidance vector selection has better performance when solving shifted and rotated optimization problems.