Image Reconstruction for Adaptive Electrical Capacitance Volume Tomography by Using Differential Evolution Algorithm

Abstract

Adaptive electrical capacitance volume tomography (AECVT) is a powerful and flexible imaging technique that has received widespread attention in recent years. However, the accuracy of reconstructed image in AECVT system is still not ideal enough to meet the needs of industrial problems. Although differential evolution (DE) algorithm has played a great role in many other areas, very few studies have noted the role it plays in electrical capacitance volume tomography (ECVT) system, let alone AECVT system. Therefore, this paper contrasts the quality of the reconstruction image among the Particle Swarm Optimization algorithm and the DE algorithm with different strategies respectively applied in AECVT system when the adaptive plates are only excited by single voltage. Then we present a modified DE algorithm with a novel mutation strategy, which has a satisfied capability of avoiding convergence to the local optimum. By preliminary discussion, the modified DE algorithm is demonstrated to be slightly superior and stable to DE algorithm for single voltage excitation. In addition, in order to make full use of the flexibility of AECVT system, we conduct a series of experiments to find the best voltage envelope for the performance of proposed algorithm.