Multi-Strategy Honey Badger Algorithm for Global Optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Honey Badger Algorithm (HBA) is a novel metaheuristic inspired by honey badger foraging.
• HBA faces challenges including slow convergence and local optima entrapment.
• Existing HBA strategies struggle with exploration-exploitation balance.

Purpose of the Study:

• To enhance the Honey Badger Algorithm (HBA) for improved optimization performance.
• Introduce the Multi-Strategy Honey Badger Algorithm (MSHBA) to address HBA limitations.
• Improve convergence speed, global search capability, and robustness.

Main Methods:

• Implemented Cubic Chaotic Mapping for enhanced initial population diversity.
• Integrated random search, elite tangential search, and differential mutation strategies.
• Applied MSHBA to IEEE CEC 2017 benchmark functions and engineering design problems.

Main Results:

• MSHBA demonstrated superior performance, excelling in 26 out of 29 IEEE CEC 2017 benchmarks.
• Statistical analysis confirmed the enhanced performance of MSHBA.
• MSHBA successfully solved four constrained engineering design problems.

Conclusions:

• The Multi-Strategy Honey Badger Algorithm (MSHBA) effectively overcomes the limitations of the original HBA.
• MSHBA offers a robust and efficient approach for complex optimization tasks.
• The proposed enhancements significantly improve global optimization capabilities and convergence.