Harris Hawk optimization algorithm with combined perturbation strategy and its application.

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Metaheuristic Algorithms

Background:

• Complex engineering optimization problems are often nonlinear, non-convex, and multimodal.
• Existing Harris Hawk Optimization (HHO) algorithms suffer from poor exploration-exploitation balance, local optima, slow convergence, and low accuracy.

Purpose of the Study:

• To propose an improved Harris Hawk Optimization algorithm with a combined perturbation strategy (HHO-CPS).
• To enhance the balance between exploration and exploitation, utilize elite information, and increase population diversity for better optimization outcomes.

Main Methods:

• Introduced an adaptive oscillatory escape energy parameter E formula for dynamic energy adjustment.
• Developed improved position update formulas for exploration and exploitation phases to expand offspring distribution and leverage elite information.
• Implemented a combined perturbation strategy to boost population diversity and convergence speed.

Main Results:

• HHO-CPS demonstrated superior performance and robustness compared to eleven other algorithms on CEC 2017, CEC 2022, and real-world engineering problems.
• Statistical analyses (Friedman rank and test) confirmed significant performance differences, validating HHO-CPS's effectiveness.
• The algorithm showed substantial improvements in solving complex engineering optimization tasks.

Conclusions:

• The proposed HHO-CPS algorithm is effective and feasible for tackling complex engineering optimization challenges.
• HHO-CPS offers significant potential for advancing engineering design optimization and innovation.
• The enhanced algorithm provides a more robust and accurate solution compared to traditional HHO.