Development and evaluation of hybrid harris hawks optimization algorithms for advanced engineering applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The standard Harris Hawk Optimizer (HHO) algorithm, while effective, suffers from slow convergence and limited exploitation capabilities, especially on high-dimensional and constrained problems.
• Existing optimization algorithms often struggle to balance exploration and exploitation effectively, leading to premature convergence or suboptimal solutions.

Purpose of the Study:

• To develop seven novel variants of the Harris Hawk Optimizer (HHO) by integrating adaptive mechanisms, chaotic dynamics, elite preservation, and cross-algorithmic hybridization.
• To enhance the balance between exploration and exploitation in HHO for improved performance on complex optimization tasks.
• To evaluate the efficacy of the proposed HHO variants against state-of-the-art optimizers on benchmark functions and engineering design problems.

Main Methods:

• Development of seven HHO variants: HHO-ADAP, HHO-CHAOS, HHO-Elite, HHO-GA, HHO-Inertia, HHO-PSO, and HHO-ULTRA.
• Rigorous testing on the CEC 2014 benchmark suite across dimensions 10, 30, 50, and 100.
• Evaluation on ten constrained engineering design problems and comparison with advanced optimizers like CMA-ES, L-SHADE, and WMA.

Main Results:

• Hybridized HHO variants consistently outperformed the baseline HHO and classical optimizers, demonstrating superior convergence speed and solution accuracy.
• HHO-PSO and HHO-Elite achieved up to 35% faster convergence and reached solution values as low as 10⁻²¹⁶.
• Variants like HHO-Elite, HHO-CHAOS, and HHO-ADAP effectively delayed stagnation and preserved diversity on multimodal functions.
• Near-optimal designs were achieved for engineering problems (e.g., pressure vessel, spring, welded beam), with low variance and significantly faster runtimes compared to leading algorithms.

Conclusions:

• Hybridization significantly enhances the robustness, solution accuracy, and adaptability of the Harris Hawk Optimizer.
• The proposed HHO variants are highly effective for solving large-scale, nonlinear, and constrained optimization problems.
• The developed algorithms offer a promising advancement for applications in engineering and scientific domains requiring efficient optimization.