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An improved enterprise development optimizer based on labor migration for numerical optimization.

Dawei Zhao1,2, Leidong Feng3, Yijiang Wang4

  • 1School of Labor Economics, Capital University of Economics and Business, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces LMEDO, an enhanced meta-heuristic algorithm that overcomes the limitations of the original Enterprise Development Optimizer (EDO). LMEDO improves convergence and search effectiveness for complex optimization problems.

Keywords:
Engineering optimization problemsEnterprise development optimizerLabor migrationMetaheuristic

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Meta-heuristic computing

Background:

  • Enterprise Development Optimizer (EDO) is a meta-heuristic algorithm with limitations in convergence and exploration-exploitation balance.
  • These limitations hinder its performance on complex optimization tasks.

Purpose of the Study:

  • To propose an improved meta-heuristic algorithm, LMEDO, addressing EDO's shortcomings.
  • To enhance convergence rate, stability, and search effectiveness in optimization.

Main Methods:

  • Integration of EDO with time-phase based switching, economy-driven guided learning, and spatial selectivity strategies.
  • Extensive evaluation using the CEC 2018 test suite and engineering optimization problems.
  • Parameter sensitivity analysis and ablation experiments to validate strategy effectiveness.

Main Results:

  • LMEDO demonstrated superior performance compared to state-of-the-art algorithms on the CEC 2018 test suite.
  • Achieved an average rank of 2.5862 and significant results in the Wilcoxon rank sum test.
  • Validated effectiveness and reliability on engineering design optimization problems.

Conclusions:

  • LMEDO is a robust and effective variant of meta-heuristic algorithms.
  • The proposed strategies significantly improve convergence, stability, and search effectiveness.
  • LMEDO shows promise for solving complex optimization problems accurately.