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Improved Egret Swarm Optimization Algorithm Based on Variable-Factor Weighted Learning and Adjacent Generation

Sunde Wang¹, Yejun Zheng², Pu Wang³

¹School of Electronics and Electrical Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

Biomimetics (Basel, Switzerland)

|June 25, 2026

Summary

This summary is machine-generated.

The improved egret swarm optimization algorithm (IESOA) enhances optimization accuracy and convergence speed for complex problems. This novel approach improves performance on engineering design tasks.

Keywords:

adjacent generation dimension crossover egret swarm optimization algorithm engineering optimization design preferred mutation reverse learning variable-factor weighted learning

Related Experiment Videos

Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

Traditional egret swarm optimization algorithm (ESOA) struggles with high-dimensional problems, exhibiting low accuracy, poor local extremum escape, and rapid diversity decay.
Existing algorithms often lack efficiency in late convergence stages, limiting their applicability to complex optimization tasks.

Purpose of the Study:

To develop an improved egret swarm optimization algorithm (IESOA) that addresses the limitations of the traditional ESOA.
To enhance optimization accuracy, global exploration, local exploitation, and convergence speed in high-dimensional complex optimization problems.

Main Methods:

Introduced a dynamic adjustment rule for core model parameters (exploration factor ω and exploitation factor μ) to balance exploration and exploitation.
Implemented a multi-individual variable-factor weighted learning mechanism to prevent premature population assimilation and retain diverse information.
Developed an adjacent generation dimension crossover strategy prioritizing absolute dimension difference for optimal individual updates.
Integrated a preferred mutation reverse learning strategy to improve local extremum escape and convergence accuracy.

Main Results:

IESOA demonstrated significant advantages in optimization accuracy, convergence speed, and stability compared to eight other algorithms on CEC2014 and CEC2019 benchmark test suites.
The algorithm was successfully applied to reinforced concrete beam design, welded beam design, and pressure vessel design problems.
IESOA effectively reduced structural design costs in engineering applications.

Conclusions:

The proposed IESOA effectively overcomes the limitations of the traditional ESOA for high-dimensional complex optimization problems.
IESOA exhibits superior performance in terms of accuracy, speed, and stability, making it a valuable tool for engineering design optimization.