Swarm intelligence algorithm based on spiking neural-oscillator networks, coupling interactions and search performances

Abstract

This paper relates to study on a new deterministic particle swarm optimization (D-PSO) called Optimizer based on Spiking Neural-oscillator Networks (OSNNs). OSNNs have a swarm consisting of plural particles which search a solution space interacting with each other. A single particle consists of plural spiking neural oscillators (`spiking oscillators') modeled by integrate-and-fire neurons. The spiking oscillators are coupled by a network topology and interact with each other by exchanging their own spike signals. Such interaction results in that coupling spiking oscillators can take synchronous or asynchronous dynamics and affects search performances of OSNNs. Herein we propose the basic algorithm of OSNNs and applied Ring 1-way network topology to coupling spiking oscillators. We theoretically analyzed parameter conditions for OSNNs, demonstrated the analytic results, and verified search performances of OSNNs through numerical simulations. We also herein discuss search performances of OSNNs and the relationship between the search performances and analytic results, and clarify prospective parameter regions which lead to good search performances in solving optimization problems.