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An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems.

Rong Zheng1, Heming Jia1, Laith Abualigah2,3

  • 1School of Information Engineering, Sanming University, Sanming 365004, China.

Mathematical Biosciences and Engineering : MBE
|March 28, 2022
PubMed
Summary
This summary is machine-generated.

The improved remora optimization algorithm (IROA) enhances global optimization by introducing an autonomous foraging mechanism. This boosts convergence speed and solution accuracy for complex problems.

Keywords:
arithmetic optimization algorithmglobal optimizationmetaheuristic algorithmremora optimization algorithmswarm intelligence

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The remora optimization algorithm (ROA) is a metaheuristic for global optimization.
  • ROA faces challenges including slow convergence, low accuracy, and local optima.
  • These limitations hinder its effectiveness in complex optimization tasks.

Purpose of the Study:

  • To propose an improved remora optimization algorithm (IROA).
  • To address the limitations of the original ROA.
  • To enhance performance in solving global optimization problems.

Main Methods:

  • Introduction of an autonomous foraging mechanism (AFM) into ROA.
  • AFM allows agents to search randomly or based on food position.
  • Performance evaluation using 23 benchmark and 10 CEC 2021 test functions.

Main Results:

  • IROA demonstrated superior performance compared to seven metaheuristic and six modified algorithms.
  • The algorithm achieved optimal results on five engineering design problems.
  • IROA effectively expanded the search space and improved solution accuracy.

Conclusions:

  • The proposed autonomous foraging mechanism significantly enhances ROA's performance.
  • IROA shows strong potential for solving real-world optimization problems.
  • The improved algorithm offers a more effective approach to global optimization.