Gaussian Quantum-Behaved Particle Swarm with Learning Automata-Adaptive Attractor and Local Search

This paper presents Gaussian Quantum-Behaved Particle Swarm Optimization (GQPSO) with a Learning Automata- Adaptive Attractor (LAAA) and a Probabilistic Local Search Mutation Operator (PLSMO). The LAAA allows the swarm to reinforce prior experience and adapt the behavior of the algorithm to a specific environment. The PLSMO was used as the supporting search to avoid premature convergence. The authors presented the numerical results of 16 benchmark functions to demonstrate that the proposed GQPSO-LALO outperforms the original QPSO and its variants in terms of global search and robustness.