Multiple strategy enhanced hybrid algorithm BAGWO combining beetle antennae search and grey wolf optimizer for global...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Existing optimization algorithms like BAS and GWO have limitations.
• Hybridization can enhance algorithm performance by combining complementary strengths.

Purpose of the Study:

• To introduce and evaluate BAGWO, a novel hybrid optimization algorithm integrating BAS and GWO.
• To improve upon original BAS and GWO strategies through enhanced mechanisms.

Main Methods:

• Integration of Beetle Antennae Search (BAS) and Grey Wolf Optimizer (GWO) into the BAGWO framework.
• Introduction of novel strategies: sigmoid-based charisma update, cosine-based local exploitation frequency, and adaptive antennae length decay.
• Validation through ablation experiments on benchmark functions (CEC 2005, CEC 2017) and real-world engineering problems.

Main Results:

• BAGWO exhibits stable convergence properties.
• Demonstrated superior optimization performance compared to existing algorithms.
• Achieved higher solution accuracy and stability in extensive testing.

Conclusions:

• BAGWO effectively leverages the strengths of BAS and GWO.
• The proposed enhancements significantly improve optimization capabilities.
• BAGWO shows strong competitiveness and potential for practical global optimization applications.