Chaotic enhanced leader slime mold algorithm for dome structures with frequency constraints

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Bio-inspired Algorithms

Background:

• Traditional optimization algorithms struggle with high-dimensional engineering problems.
• Slime mold algorithm (SMA) offers a bio-inspired approach but can be improved.
• Premature convergence and limited exploration/exploitation are common challenges.

Purpose of the Study:

• Introduce the Chaotic Enhanced Leader Slime Mold Algorithm (CELSMA) for complex engineering optimization.
• Enhance exploration and exploitation capabilities beyond traditional SMA.
• Improve global search behavior and mitigate premature convergence.

Main Methods:

• CELSMA utilizes a multi-leader strategy with three candidate leaders.
• Incorporates chaotic maps to leverage ergodic and non-repetitive search properties.
• Applies the Largest Eigenvalues of Sparse Matrix (LESM) technique to accelerate eigenvalue computation for truss structures.

Main Results:

• CELSMA-LESM demonstrated superior accuracy and convergence speed on large-scale dome truss structures.
• The algorithm consistently achieved optimal solutions in fewer iterations compared to benchmarks.
• Significant reduction in computational time was observed due to the LESM technique.

Conclusions:

• CELSMA represents an advanced bio-inspired optimization technique for high-dimensional engineering challenges.
• The integration of chaotic maps and multi-leader strategy enhances optimization performance.
• The CELSMA-LESM approach offers a computationally efficient and effective solution for truss structure optimization.