Toward the modification of flower pollination algorithm in clustering-based image segmentation

Flower pollination algorithm (FPA) is a new bio-inspired optimization algorithm, which has shown an effective performance on solving many optimization problems. However, the effectiveness of FPA significantly depends on the balance achieved by the exploration and exploitation evolutionary stages. Since purely exploration procedure promotes non-accurate solutions, meanwhile, purely exploitation operation promotes sub-optimal solutions in the presence of multiple optima. In this study, three global search and two local search strategies have been designed to improve balance among evolutionary stages, increasing the efficiency and robustness of the original FPA methodology. Additionally, some parameter adaptation techniques are also incorporated in the proposed methodology. The modified FPA has been successfully applied for histopathological image segmentation problem. The experimental and computational effort results indicate its effectiveness over existing swarm intelligence algorithms and machine learning methods.