Adaptive Parameter Selection of Quantum-Behaved Particle Swarm Optimization on Global Level

Abstract

In this paper, we formulate the philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the population level. After that, we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization Algorithm (AQPSO). Finally, the performance of AQPSO algorithm is compared with those of Standard PSO (SPSO) and original QPSO by testing the algorithms on several benchmark functions. The experiments results show that AQPSO algorithm outperforms due to its strong global search ability, particularly in the optimization problems with high dimension.