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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Metaheuristic Optimization

Background:

  • The marine predators algorithm (MPA) is a population-based optimization method.
  • MPA can suffer from premature convergence and local optima due to reduced population diversity.

Purpose of the Study:

  • To propose a novel variant of the MPA, termed HEGMPA.
  • To enhance the diversity and convergence capabilities of the MPA.

Main Methods:

  • Hybridizing MPA with Estimation Distribution Algorithm (EDA) and a Gaussian random walk strategy.
  • Utilizing cubic mapping for initial population generation to boost diversity.
  • Employing EDA to guide evolutionary direction based on population distribution.
  • Incorporating a Gaussian random walk with a medium solution to escape stagnation.

Main Results:

  • HEGMPA demonstrated superior performance compared to other algorithms on the CEC2014 test suite.
  • Significant improvements in convergence accuracy were observed.
  • Enhanced convergence speed was a key outcome of the proposed hybrid approach.

Conclusions:

  • HEGMPA effectively addresses the local optimum problem in MPA.
  • The hybrid strategy significantly improves convergence accuracy and speed.
  • HEGMPA offers a more competitive and robust optimization solution.