Enhanced Aquila optimizer algorithm for global optimization and constrained engineering problems.

Area of Science:

• Computational Intelligence
• Swarm Intelligence Algorithms
• Optimization Techniques

Background:

• The Aquila Optimizer (AO) is a nature-inspired swarm algorithm simulating avian hunting behaviors.
• Standard AO can exhibit slow convergence and suboptimal solutions in complex optimization landscapes.

Purpose of the Study:

• To enhance the performance of the Aquila Optimizer (AO) for complex optimization problems.
• To address limitations such as slow convergence and susceptibility to local optima.

Main Methods:

• Integration of three strategies: restart strategy, opposition-based learning, and chaotic local search.
• Development of the modified Aquila Optimizer (mAO).
• Empirical validation using 29 CEC 2017 benchmark functions and five constrained engineering problems.

Main Results:

• The mAO algorithm demonstrated significant improvements in convergence speed and solution quality.
• Superior performance of mAO compared to the standard AO across diverse and complex optimization challenges.
• Effective handling of constrained engineering problems, showcasing practical applicability.

Conclusions:

• The proposed mAO algorithm effectively overcomes the limitations of the standard AO.
• mAO offers a robust and efficient approach for tackling complex optimization problems.
• The hybrid strategy significantly boosts the optimization capabilities of swarm intelligence algorithms.