A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics

Mohamed Abd Elaziz, Dalia Yousri, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a modified version of a contemporary metaheuristic named Harris Hawks Optimizer (HHO) that mimics the foraging strategies used by Harris hawks. It is first argued that exploration ability of HHO is weaker than its exploitation. In addition, the initial position of hawks has the greatest impact on the convergence of the solutions in a similar manner to other metaheuristic algorithms. Then, we applied the Fractional-Order Gauss and 2xmod1 Chaotic Maps to generate the initial population as well as applying the operators of the Moth-Flame Optimization (MFO) to improve the exploration of HHO. In addition, the concept of evolutionary Population Dynamics (EPD) is applied to prevent premature convergence and stagnation in local optima. The method proposed in this work is called FCHMD and evaluated using a set of thirty-six mathematical functions and five engineering problems. The results of the FCHMD are compared with a number of well-known metaheuristics. It can be observed that the FCHMD algorithm outperforms its competitors on the majority of case studies.

Original languageEnglish
Article number102973
JournalAdvances in Engineering Software
Volume154
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Algorithm
  • Evolutionary population dynamics
  • Grey Wolf Optimizer
  • Harris hawks optimizer
  • Moth-flame optimization
  • Optimization

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