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mESC: An Enhanced Escape Algorithm Fusing Multiple Strategies for Engineering Optimization.

Jia Liu1, Jianwei Yang2, Lele Cui3

  • 1Faculty of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723000, China.

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Summary
This summary is machine-generated.

A new multi-strategy enhanced escape algorithm (mESC) improves optimization by balancing exploration and development, achieving higher accuracy. This enhanced algorithm outperforms existing methods on benchmark tests and real-world problems.

Keywords:
escape algorithmmeta-heuristic algorithmmulti-strategy enhanced versionrealistic optimization problems

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • The standard escape algorithm (ESC) faces challenges in balancing exploration and development, leading to low convergence accuracy.
  • Maintaining population diversity and enhancing exploration capabilities are critical for effective optimization algorithms.

Purpose of the Study:

  • To propose a multi-strategy enhanced escape algorithm (mESC) that addresses the limitations of the original ESC.
  • To improve the balance between exploration and development stages in optimization processes.
  • To enhance convergence accuracy and global convergence speed.

Main Methods:

  • Implemented an adaptive perturbation factor strategy to maintain population diversity.
  • Introduced a restart mechanism to bolster the exploration capability of the algorithm.
  • Designed a dynamic centroid reverse learning strategy for balanced local development.
  • Developed a boundary adjustment strategy using an elite pool to accelerate global convergence.

Main Results:

  • The mESC algorithm demonstrated superior performance compared to the latest metaheuristic and high-performance winner algorithms on the CEC2022 testing suite.
  • Numerical results confirmed the enhanced performance and effectiveness of mESC.
  • The algorithm's superiority was validated on several classic real-world optimization problems.

Conclusions:

  • The proposed mESC algorithm effectively addresses the limitations of the standard ESC, particularly in balancing exploration and development.
  • mESC exhibits improved convergence accuracy and global convergence speed.
  • The enhanced algorithm shows significant potential for solving complex real-world optimization challenges.