IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization...

Area of Science:

• Computational Intelligence
• Meta-heuristic Optimization
• Nature-Inspired Algorithms

Background:

• Aquila Optimizer (AO) excels in global exploration but lacks stable local exploitation.
• African Vultures Optimization Algorithm (AVOA) offers strong exploitation but limited exploration.
• Existing single algorithms have inherent limitations in addressing complex optimization challenges.

Purpose of the Study:

• To develop an improved hybrid meta-heuristic algorithm, the Aquila Vulture Optimizer (AVO).
• To overcome the deficiencies of individual AO and AVOA algorithms.
• To enhance solution quality for global optimization problems.

Main Methods:

• Hybridization of AO's exploration and AVOA's exploitation phases.
• Integration of Composite Opposition-Based Learning (COBL) for increased diversity and escaping local optima.
• Application of Fitness-Distance Balance (FDB) strategy to refine the position update mechanism.

Main Results:

• The proposed AVO algorithm demonstrated superior solution accuracy and convergence speed compared to AO, AVOA, and six other state-of-the-art algorithms.
• AVO exhibited enhanced ability to avoid local optima across 23 benchmark functions and the IEEE CEC2019 test suite.
• The algorithm proved effective in solving five complex engineering design problems, showcasing its practical applicability.

Conclusions:

• The hybrid Aquila Vulture Optimizer (AVO) effectively addresses the limitations of its constituent algorithms.
• AVO offers a competitive and promising approach for tackling both theoretical and real-world optimization tasks.
• The source code for AVO is publicly available, facilitating further research and application.