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

Dalia Yousri, Mohamed Abd Elaziz, Seyedali Mirjalili

Research output: Contribution to journalArticle


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.

Original languageEnglish
Article number105889
JournalKnowledge-Based Systems
Publication statusPublished - 7 Jun 2020



  • Algorithm
  • Benchmark
  • Flower pollination algorithm
  • Fractional calculus
  • Image segmentation
  • Local search
  • Meta-heuristic
  • Optimization

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