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Updated: Jul 15, 2025

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Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

Hongwei Ding1, Yuting Liu1, Zongshan Wang1

  • 1School of Information Science and Engineering, Yunnan University, Kunming 650106, China.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
Summary

This study enhances the equilibrium optimizer (EO) algorithm to improve its performance on complex problems. The enhanced EO shows superior results in numerical and practical applications, offering a more robust optimization tool.

Keywords:
equilibrium optimizerglobal optimizationmetaheuristicsmobile robot path planningnature-inspired

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Metaheuristic computing

Background:

  • The equilibrium optimizer (EO) is a physics-based algorithm for complex optimization.
  • Existing EO methods suffer from local optima and limited population diversity.
  • Addressing these limitations is crucial for enhancing optimization performance.

Purpose of the Study:

  • To propose an enhanced equilibrium optimizer (EO) algorithm.
  • To improve population diversity and avoid local optima in optimization.
  • To evaluate the effectiveness of the enhanced EO on benchmark and real-world problems.

Main Methods:

  • Introduced a spiral search mechanism for guided particle movement.
  • Employed a novel inertia weight factor to reduce particle oscillation.
  • Tested the enhanced EO on the CEC2017 test suite and mobile robot path planning (MRPP).

Main Results:

  • The enhanced EO algorithm demonstrated superior performance compared to other metaheuristic techniques.
  • Experimental results confirmed improved solutions for both numerical and practical optimization tasks.
  • The improved EO variant exhibited significant robustness and stability.

Conclusions:

  • The enhanced equilibrium optimizer effectively addresses limitations of the original EO.
  • The spiral search and new inertia weight factor contribute to better optimization.
  • The developed EO variant is a promising tool for complex optimization challenges.