Neighborhood immune based Guiding Quantum Particle Swarm Optimizer

In this paper, a variant of quantum particle swarm optimizer called Guiding Quantum Particle Swarm Optimizer incorporating Immune algorithm (GQPSOI) is proposed. The algorithm employs quantum infusion to train the effectiveness of learning as particle swarm optimization with Quantum Infusion (PSO-QI). Differing from PSO-QI, GQPSOI replaces particles by means of neighborhood-based immune algorithm which helps to preserve the swarm diversity according to particles´ fitness and concentration. In order to restrain premature convergence and escape from the local minimum, guidance particle was introduced according to the global optimum value. Several classical nonlinear functions are employed to test the effectiveness of GQPSOI.