A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching

Abstract

A novel Chaotic Quantum-behaved Particle Swarm Optimization Based on Lateral Inhibition (LI-CQPSO) is proposed in this paper, which is used to solve complicated image matching problems. As one of the meta-heuristic algorithms inspired by biological behaviors, Particle Swarm Optimization (PSO) has been successfully applied to image matching. However, high computational complexity and premature convergence of PSO are the main drawbacks that limit its further application. In this work, the proposed LI-CQPSO which combines advantages of chaos theory, quantum and lateral inhibition could have better performance. Chaos can guarantee the PSO escaping from local best, quantum can make the traditional PSO with better searching performance as well as having fewer parameters to control, and lateral inhibition is applied to extract the edge of the images by sharpening the spatial profile of excitation in response to a localized stimulus. The detailed process of LI-CQPSO is also given. The effectiveness and feasibility of the proposed algorithm are illustrated in solving image matching problems by series of comparative experiments with PSO, QPSO, and LI-PSO.