Gaussian Quantum-Behaved Particle Swarm with Learning Automata-Adaptive Attractor and Local Search
Sivakorn Sansawas, Tanathep Roongpipat, Saksorn Ruangtanusak, Jessada Chaikhet, Chukiat Worasucheep, Warin Wattanapornprom · 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · 2022
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-LALOoutperforms the original QPSO and its variants in terms of global search and robustness.