Hybrid-Flash Butterfly Optimization Algorithm with Logistic Mapping for Solving the Engineering Constrained...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• The standard butterfly optimization algorithm (BOA) relies solely on smell perception, limiting its effectiveness and leading to local optima.
• Real butterfly foraging behavior involves both smell and color perception, suggesting potential for algorithmic improvement.

Purpose of the Study:

• To introduce a novel hybrid-flash butterfly optimization algorithm (HFBOA) that incorporates color perception.
• To enhance the global optimization capabilities of the BOA through logistic mapping for control parameter updates.

Main Methods:

• The HFBOA integrates a color perception rule, mimicking natural butterfly foraging, into the existing BOA framework.
• Logistic mapping is employed to dynamically update control parameters, aiming to improve global search performance.
• The algorithm's efficacy is validated using twelve benchmark functions and six engineering constrained optimization problems.

Main Results:

• HFBOA demonstrates faster convergence and superior stability compared to six state-of-the-art optimization methods on benchmark functions.
• The algorithm achieves excellent performance in solving complex, real-world engineering constrained optimization problems, including tubular column, spring, and beam designs.

Conclusions:

• The proposed HFBOA effectively addresses the local optimum limitations of the original BOA.
• The integration of color perception and logistic mapping significantly enhances optimization performance for both numerical and engineering applications.