Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems

In this paper, we focus on solving non-linear programming (NLP) problems using quantum-behaved particle swarm optimization (QPSO). After a brief introduction to the original particle swarm optimization ( PSO), we describe the origin and development of QPSO, and the penalty function method for constrained NLP problems. The performance of QPSO is tested on some unconstrained and constrained benchmark functions and compared with PSO with inertia weight (PSO-In) and PSO with constriction factor (PSO-Co). The experimental results show that QPSO outperforms the traditional PSOs and is a promising optimization algorithm.