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

Automated summary

This paper proposes a novel technique for handling big data optimization problems by detecting actual EEG signals while eliminating additional brain activity patterns. The study compares the performance of different algorithms on various datasets and statistically evaluates the results, finding that IPSO-Q can serve as an alternative algorithm for solving Big(Opt) problems.