A Simple and Fast Particle Swarm Optimization and Its Application on Portfolio Selection

Particle Swarm Optimization (PSO) has shown its good performance on well-known numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in diversity. Some diversity-guided PSO algorithms have proposed to maintain diversity, while these techniques cost much computation time on the calculation of diversity. In this paper, a simple and fast PSO (hybrid PSO, HPSO) is proposed, which indirectly maintains the diversity of swarm but not compute it. Experimental studies on 8 well-known benchmark functions and a portfolio selection optimization problem show that the HPSO does not only obtain better performance than the standard PSO and other two diversity-guided PSO algorithms, but almost cost the same computation time with the standard PSO.