Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

Introducing a novel approach to enhance the diversification and intensification propensities of the flower pollination algorithm (FPA) is the main aim of this paper. Therefore, the fractional-order (FO) calculus features are adopted to enhance the basic FPA local search ability and adaptive modify the harmonization coefficient among the FPA exploration and exploitation cores. The proposed Fractional-order FPA (FO-FPA) is examined in a number of experiments. Firstly, FO-FPA is tested with thirty-six benchmark functions with several dimensions. The proposed FO-FPA is compared with recent proved techniques based on several statistical measures and non parametric tests. Secondly, FO-FPA is implemented for a real application of the image segmentation and its results compared with state-of-the-art multi-level thresholding algorithms. The comparisons divulge the remarkable influence of merging FO with the basic FPA in improving the quality of the solutions and the acceleration of the convergence speed.