An improved Quantum-behaved Particle Swarm Optimization with Random Selection of the Optimal Individual

The random selection of optimal individual is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, and a linear weight parameter which implied the importance of particles in population according to their fitness value is used in QPSO to balance the global and local searching abilities while having better convergence speed at the same time, and proposes a revised QPSO algorithm. To evaluate the performance of the new method, the revised QPSO and QPSO are tested on several benchmark functions; experiment results show that the revised QPSO has better performance than QPSO.