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Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy.

Meijia Song1, Heming Jia2, Laith Abualigah3,4

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Harris hawks optimization (HHO) can get stuck in local optima. This study introduces an enhanced Harris hawks optimization (ERHHO) using tent chaotic maps, an exploration factor, and random walks to improve global optimization performance and reliability.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Harris hawks optimization (HHO) is a popular population-based metaheuristic algorithm.
  • HHO effectively imitates natural hunting behaviors but often suffers from premature convergence and stagnation in local optima, limiting its global optimization capabilities.

Purpose of the Study:

  • To propose an improved Harris hawks optimization algorithm, termed ERHHO, designed to overcome the local optima stagnation issue.
  • To enhance the global search capability and reliability of the Harris hawks optimization algorithm for complex optimization problems.

Main Methods:

  • Introduced a tent chaotic map during the initialization phase to increase population diversity.
  • Developed an exploration factor to optimize algorithm parameters, thereby improving exploration ability.
  • Incorporated a random walk strategy to boost exploitation capabilities and facilitate escaping local optima.

Main Results:

  • Systematic experiments were conducted on 23 benchmark functions and the CEC2017 test suite.
  • The proposed ERHHO algorithm demonstrated superior performance compared to other well-established algorithms.
  • ERHHO provided more reliable solutions, effectively addressing the local optima problem inherent in standard HHO.

Conclusions:

  • The ERHHO algorithm successfully enhances the Harris hawks optimization by improving population diversity, exploration, and exploitation.
  • ERHHO offers a more robust and reliable approach for solving global optimization problems, outperforming existing methods.
  • The proposed enhancements effectively mitigate local optima stagnation, paving the way for more effective metaheuristic optimization.