Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Aquila Optimizer (AO) is effective but can have limited exploration.
• Existing metaheuristic algorithms may struggle with noisy objective functions.

Purpose of the Study:

• To enhance the exploration capability and noise robustness of the Aquila Optimizer (AO).
• To develop a hybrid optimization algorithm by integrating Grey Wolf Optimizer (GWO) and quasi-opposition-based learning (QOBL) with AO.

Main Methods:

• A hybrid approach combining AO with GWO's alpha position for search guidance.
• Application of quasi-opposition-based learning (QOBL) in each phase of the AO algorithm.
• Benchmarking the proposed hybrid algorithm on 23 standard test functions and CEC2017 test functions.

Main Results:

• The hybrid AO-GWO-QOBL algorithm demonstrated superior performance compared to other metaheuristic algorithms.
• The enhanced algorithm showed excellent efficacy on benchmark and engineering problems.
• The integration of GWO improved AO's robustness to noisy objective functions.

Conclusions:

• The proposed hybrid optimization technique effectively addresses the exploration limitations of the AO algorithm.
• The integration of GWO and QOBL significantly improves the performance and robustness of the Aquila Optimizer.
• The enhanced algorithm is suitable for solving complex engineering problems with uncertain search spaces.