Improved Multi-Strategy Aquila Optimizer for Engineering Optimization Problems.

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization Algorithms

Background:

• The Aquila Optimizer (AO) is an efficient metaheuristic but struggles with high-dimensional, complex problems due to premature convergence.
• Existing AO variants and other state-of-the-art algorithms have limitations in addressing these challenges.

Purpose of the Study:

• To propose the Multi-Strategy Aquila Optimizer (MSAO) to overcome the limitations of the standard AO.
• To enhance the exploration and exploitation capabilities of the AO for complex optimization tasks.

Main Methods:

• Integration of a random sub-dimension update mechanism for improved exploration in high-dimensional spaces.
• Incorporation of memory and dream-sharing strategies from the Dream Optimization Algorithm (DOA) for balanced exploration and exploitation.
• Application of adaptive parameters and dynamic opposition-based learning to refine AO update rules within a multi-strategy framework.

Main Results:

• The MSAO demonstrated superior performance compared to eight state-of-the-art algorithms on benchmark functions, achieving top results on 55-72% of them.
• Ablation experiments confirmed the significant contribution of each newly introduced strategy.
• Successful application of the MSAO to five engineering problems highlighted its practical value.

Conclusions:

• The proposed MSAO effectively addresses the limitations of the standard AO, particularly in high-dimensional and complex optimization scenarios.
• The integration of multiple strategies results in a robust and high-performing optimization algorithm.
• The MSAO shows significant potential for practical applications in various engineering fields.