The heterogeneous Aquila optimization algorithm.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The Aquila Optimizer (AO) is a recent swarm-based algorithm demonstrating strong performance.
• A limitation of AO is its slow convergence in later iterations, hindering its efficiency.

Purpose of the Study:

• To enhance the Aquila Optimizer (AO) by introducing a multiple updating principle, creating the Heterogeneous Aquila Optimizer (HAO).
• To evaluate the performance of HAO against other algorithms on benchmark functions and real-world engineering problems.

Main Methods:

• Development of the Heterogeneous Aquila Optimizer (HAO) by incorporating a multiple updating principle into the AO framework.
• Simulation experiments on unimodal and multimodal benchmark functions.
• Comparative analysis with existing optimization algorithms.
• Application to three real-world engineering optimization problems.

Main Results:

• HAO demonstrated superior performance compared to other algorithms on benchmark functions.
• The enhanced algorithm exhibits improved intensification and diversification capabilities.
• HAO achieved a fast convergence rate with low residual errors.
• Strong scalability and convincing verification results were observed.
• Effective performance was confirmed in optimizing real-world engineering problems without additional equations.

Conclusions:

• The Heterogeneous Aquila Optimizer (HAO) effectively addresses the limitations of the original Aquila Optimizer (AO), particularly its slow convergence.
• HAO offers enhanced intensification and diversification, leading to improved optimization performance.
• The algorithm's efficacy is validated on both theoretical benchmark functions and practical engineering challenges.