Hybridization of moth flame optimization and gravitational search algorithm and its application to detection of food quality

Abstract

Gravitational search algorithm (GSA) is an optimization algorithm inspired from Newton's law of gravitation. Moth flame optimization (MFO) is another optimization algorithm, motivated by the locomotion of moths around a light source. Both of these algorithms have tried to model the search agents and altered properties like mass, gravitational constant, fitness, location, etc. in order to find the most optimal value. Optimization algorithms usually solve only a class of problems and therefore the search for a faster and more comprehensive algorithm is always on. By hybridizing MFO and GSA, the performance is expected to improve across various measures. This paper presents a hybrid optimization algorithm by using concepts of moth flame optimization and gravitational search algorithm and applies this hybrid algorithm to image segmentation. An optimized K-means algorithm and an optimized thresholding algorithm have been proposed. The results of the segmentation are then used to classify apples into different classes.