Dynamic quantum-inspired particle swarm optimization as feature and parameter optimizer for evolving spiking neural networks

Abstract

This paper proposes a new structure for Quantum-inspired Particle Swarm Optimization (QiPSO) to enhance feature and parameter optimization of Evolving Spiking Neural Networks (ESNN). The new Dynamic Quantum-inspired Particle Swarm Optimization (DQiPSO) will be integrated within ESNN where features and parameters are simultaneously and more efficiently optimized. The features are modeled as a quantum bit vector, where probability computation is added to perform the feature selection task. For the parameters, values are presented as real numbers. A hybrid particle structure is required for these two different data types. In addition, an improved search strategy has been introduced to find the most relevant features and eliminate irrelevant features on a synthetic dataset. The results show that the proposed optimizer structure yields promising outcomes in identifying the most relevant features, and obtaining the best combination of ESNN parameters with faster and more accurate classification.