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A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

Yuting Liu1, Hongwei Ding1, Zongshan Wang1

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

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|November 3, 2023
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Summary
This summary is machine-generated.

A new chaos-based adaptive equilibrium optimizer (ACEO) algorithm improves upon the original equilibrium optimizer (EO). ACEO enhances exploration and exploitation, effectively solving complex optimization problems like mobile robot path planning.

Keywords:
chaosequilibrium optimizerglobal optimizationmetaheuristic algorithmsrobot path planning

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Equilibrium Optimizer (EO) is a physics-based algorithm with strengths in simplicity and effectiveness.
  • However, the standard EO struggles with balancing exploration/exploitation and can get trapped in local optima.

Purpose of the Study:

  • To address the limitations of the canonical EO algorithm.
  • To propose a novel optimization approach, the Chaos-based Adaptive Equilibrium Optimizer (ACEO).

Main Methods:

  • Introducing an adaptive gbest-guided search mechanism to enhance population diversity and search range.
  • Incorporating a chaos mechanism to aid in escaping local optima.
  • Evaluating ACEO on 23 benchmark functions and a mobile robot path planning (MRPP) task.

Main Results:

  • ACEO demonstrated superior performance compared to the canonical EO and other advanced metaheuristic algorithms.
  • The algorithm showed significant improvements in exploration-exploitation balance and convergence accuracy.
  • ACEO successfully provided high-quality solutions for the mobile robot path planning problem.

Conclusions:

  • The proposed Chaos-based Adaptive Equilibrium Optimizer (ACEO) effectively overcomes the drawbacks of the original EO.
  • ACEO offers a robust and efficient approach for complex optimization tasks, including path planning.