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Multi‑strategy Equilibrium Optimizer: An improved meta-heuristic tested on numerical optimization and engineering

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The improved Equilibrium Optimizer (IEO) enhances global optimization by using Tent mapping, nonlinear time parameters, and Lens Opposition-based Learning. This intelligent optimization algorithm overcomes local optima for better accuracy and efficiency in complex problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms

Background:

  • The Equilibrium Optimizer (EO) is a novel algorithm for global optimization.
  • EO can get stuck in local optima for complex numerical and engineering problems, reducing accuracy.

Purpose of the Study:

  • To propose an improved Equilibrium Optimizer (IEO) addressing the limitations of the original EO.
  • To enhance the accuracy, robustness, and efficiency of the optimization process.

Main Methods:

  • Introduced Tent mapping for diverse initial population distribution.
  • Implemented a nonlinear time parameter to balance exploration and exploitation.
  • Incorporated Lens Opposition-based Learning (LOBL) to improve population diversity and avoid local optima.

Main Results:

  • IEO demonstrated superior performance on 23 classical, IEEE CEC2017, and IEEE CEC2019 benchmark problems.
  • Statistical analysis (Friedman test, box plots) confirmed the algorithm's stability and robustness.
  • IEO achieved high optimization efficiency and cost minimization on six engineering design problems.

Conclusions:

  • The proposed IEO effectively overcomes local optima and improves solution accuracy.
  • IEO exhibits excellent robustness and efficiency for complex optimization tasks.
  • The multi-strategy approach significantly enhances the capabilities of the Equilibrium Optimizer.