Improved Particle Swarm Optimization on Based Quantum Behaved Framework for Big Data Optimization

In recent times, big data has become an essential concern with the rapid increase of digitalization. The problems that find solutions to finding and evaluating features are called optimization problems. this paper, sets containing EEG signals have been studied. goal is detect actual while eliminating additional brain activity patterns in collected data, resulting more accurate interpretation. study, handle (BigOpt) difficulties, a novel swarm intelligence-based technique developed. A Developed PSO-Q was proposed by updating random walking phase Particle Swarm Optimization on combined quantum behaved method (PSO-Q) for BigOpt PSO-Q's local search capability improved. success IPSO-Q thoroughly tested various cycles (maximum iterations) (300, 400, 500, 1000) population sizes (10, 25, 50) six sets. outcomes were statistically evaluated Wilcoxon Signed-Rank Test. compared newly developed swarm-based algorithms (BA, Jaya, AOA, etc.) literature years. shown results obtained. showed can be used as alternative algorithm