Application of vision measurement model with an improved moth-flame optimization algorithm

Abstract

An improved moth-flame optimization (IMFO) algorithm is proposed to increase the location accuracy of a vision measurement system. This algorithm can optimize the initial pose parameters by improving a series of random solutions to the required precision. A measurement experiment system of space manipulator is designed to precision test. The IMFO algorithm is evaluated on 23 benchmark functions and measurement experiments for pose, and the results are verified by a comparative study with self-adaptive differential evolution (SaDE), moth-flame optimization (MFO), and proactive particle swarm optimization (PPSO). The statistical results of the benchmark functions show that the IMFO algorithm can provide very promising and competitive results. Additionally, the experimental results of pose measurement show that the accuracy of the IMFO algorithm is approximately twice higher than that of other three algorithms. All in all, the experiments indicate that the IMFO algorithm has a good optimization ability to complete the visual identification accurately.