Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field.