Abstract
Particle Swarm Optimization (PSO) is one of the evolutionary computations. Position vectors of particles in a swarm represent candidate solutions of the optimization problem. The position vectors, in the original PSO, are updated with the position vectors of the global and personal best particles. The global and personal best particles denote the particle that all particles and each particle have found ever during search process, respectively. In this study, particle position vectors are updated with the second global or personal best particles, in addition to the position vectors of the global and personal best particles. The present PSO algorithms are applied for the truss structure optimization problem. The results reveal that the present algorithm can find better optimal solution than that of the original PSO.
