Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering...

Area of Science:

• Optimization techniques
• Metaheuristics
• Computational intelligence

Background:

• Balancing exploration and exploitation is crucial for nature-inspired optimization methods.
• The Aquila Optimizer (AO) can suffer from premature convergence, leading to suboptimal solutions.
• Existing methods struggle to maintain optimal search dynamics.

Purpose of the Study:

• To improve the synergy between exploration and exploitation in the Aquila Optimizer.
• To develop a novel metaheuristic algorithm that escapes local stagnation.
• To enhance the global optimization capabilities of nature-inspired algorithms.

Main Methods:

• Integration of Dynamic Random Walk (DRW) to improve exploration.
• Application of Dynamic Oppositional Learning (DOL) to maintain exploration-exploitation balance.
• Development of the Dynamic Aquila Optimizer (DAO) algorithm.

Main Results:

• The proposed DOL-inspired DRW technique demonstrates higher exploration potential and computational efficiency.
• DAO effectively prevents premature convergence, leading to better optimum identification.
• DAO shows superior performance compared to existing metaheuristic algorithms on benchmark functions and engineering problems.

Conclusions:

• The Dynamic Aquila Optimizer (DAO) offers a robust solution for global optimization challenges.
• DAO successfully addresses the limitations of the original Aquila Optimizer.
• The enhanced exploration and exploitation balance in DAO leads to improved convergence and solution quality.