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Multi-strategy enterprise development optimizer for numerical optimization and constrained problems.

Xinyu Cai1, Weibin Wang2, Yijiang Wang3

  • 1College of Business, Jiaxing University, Jiaxing, 314001, China.

Scientific Reports
|March 28, 2025
PubMed
Summary
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The Multi-Strategy Enterprise Development Optimizer (MSEDO) enhances the original EDO algorithm by improving population diversity and exploitation. MSEDO effectively solves complex optimization problems and escapes local optima.

Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Metaheuristic computing

Background:

  • The Enterprise Development Optimizer (EDO) algorithm exhibits strong global search but suffers from reduced population diversity and weak exploitation in complex problems.
  • Existing EDO algorithmic structures present opportunities for optimization process enhancement.

Purpose of the Study:

  • To propose an improved metaheuristic algorithm, the Multi-Strategy Enterprise Development Optimizer (MSEDO), addressing the limitations of the basic EDO.
  • To enhance the search agent quality, population diversity, and local exploitation capabilities of the EDO algorithm.

Main Methods:

  • Introduction of a leader-based covariance learning strategy to guide search agents and maintain diversity.
  • Implementation of a fitness and distance-based leader selection strategy for dynamic local exploitation improvement.
Keywords:
CEC2017CEC2022Engineering optimization problemsEnterprise development optimizerMetaheuristic algorithmsRestart strategy

Related Experiment Videos

  • Reconstruction of the EDO algorithm structure with a diversity-based population restart strategy to escape local optima.

    • Main Results:

    • Ablation experiments confirmed the effectiveness of the individual strategies within MSEDO.
    • Comparative analysis against five other metaheuristic algorithms demonstrated MSEDO's superior performance.
    • Experimental results on CEC2017, CEC2022, and ten engineering constrained problems validated MSEDO's ability to escape local optima and solve complex real-world problems.

    Conclusions:

    • MSEDO effectively overcomes the limitations of the basic EDO algorithm, particularly in maintaining population diversity and exploitation.
    • The proposed MSEDO algorithm demonstrates robust performance in escaping local optima and solving complex, constrained engineering optimization problems.