An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation.

Area of Science:

• Manufacturing and Metrology
• Computational Intelligence
• Optimization Algorithms

Background:

• Deviation zone evaluation is critical for quality control but is a complex nonlinear problem.
• Traditional numerical optimization methods struggle with the nonlinearity and complexity of deviation zone evaluation.
• Swarm intelligence offers gradient-free, high-quality solutions with easier implementation for such problems.

Purpose of the Study:

• To develop an improved swarm intelligence algorithm for accurate deviation zone evaluation.
• To address the limitations of traditional methods in solving nonlinear optimization problems in metrology.
• To enhance the Harris Hawks Algorithm (HHO) for superior performance in quality control applications.

Main Methods:

• An improved Harris Hawks Algorithm (HHO) was developed, integrating Salp Swarm Algorithm (SSA) features.
• The random operator in HHO's exploration phase was replaced with average fitness to mitigate strategy conflicts.
• SSA's nonlinear inertia weight and explorative capabilities were embedded into HHO.
• A greedy selection strategy was employed between HHO and SSA-based individuals for optimal solution selection.

Main Results:

• The improved HHO demonstrated effectiveness on benchmark problems compared to other swarm intelligence methods.
• Experimental results showed accurate evaluation of various form deviations on primitive geometries.
• The algorithm provided an effective general solution for form deviation-zone evaluation in manufacturing and metrology.

Conclusions:

• The proposed improved HHO algorithm offers a robust and accurate solution for deviation zone evaluation.
• This method enhances quality control in manufacturing and metrology by effectively handling complex nonlinear problems.
• The algorithm provides a generalizable approach for assessing form deviations on primitive geometries.