Rules embedded harris hawks optimizer for large-scale optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Harris Hawks Optimizer (HHO) is a metaheuristic algorithm applied to various problems.
• Large-scale optimization challenges require effective exploration/exploitation balancing to avoid local optima stagnation.

Purpose of the Study:

• To enhance the search efficiency of HHO for large-scale problems.
• To develop adaptive switching rules for exploration/exploitation balancing in HHO.

Main Methods:

• Formulated embedded rules based on population status, success rate, and iteration count for adaptive switching.
• Evaluated the Rules Embedded Harris Hawks Optimizer (REHHO) on high-dimensional benchmark functions (1000-D to 10,000-D) and the CEC'2010 large-scale benchmark.
• Applied REHHO to a real-world high-dimensional wavelength selection problem.

Main Results:

• REHHO demonstrated significant improvements in accuracy and convergence speed compared to the standard HHO.
• REHHO achieved superior performance across all tested benchmark problems.
• The proposed embedded rules led to faster convergence rates.

Conclusions:

• The embedded rules effectively enhance HHO's performance in large-scale optimization.
• REHHO outperforms not only HHO but also several other state-of-the-art optimization algorithms.
• REHHO offers a robust and efficient approach for tackling complex, high-dimensional optimization tasks.