An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Global optimization problems are prevalent in various scientific and engineering domains.
• Existing metaheuristic algorithms like Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) have limitations in exploration and exploitation.
• Enhancing search diversity and avoiding local optima are critical for effective optimization.

Purpose of the Study:

• To introduce an improved hybrid metaheuristic algorithm, the IHAOHHO, for superior global optimization.
• To enhance the exploration and exploitation capabilities of AO and HHO through novel strategies.
• To validate the effectiveness and practicality of the IHAOHHO on benchmark functions and engineering problems.

Main Methods:

• A hybrid approach combining Aquila Optimizer (AO) and Harris Hawks Optimization (HHO).
• Integration of representative-based hunting (RH) strategy in the exploration phase.
• Incorporation of opposition-based learning (OBL) strategy in the exploitation phase.
• Comprehensive testing on standard and CEC2017 benchmark functions and engineering design problems.

Main Results:

• The proposed IHAOHHO algorithm exhibits enhanced global search performance.
• IHAOHHO demonstrates a faster convergence speed compared to basic AO, HHO, and other state-of-the-art algorithms.
• Experimental results validate the superior optimization capabilities and practicability of the IHAOHHO.

Conclusions:

• The IHAOHHO algorithm effectively improves search space diversity and local optima avoidance.
• The hybrid AO-HHO approach with RH and OBL strategies offers significant advantages for global optimization.
• IHAOHHO represents a promising advancement in metaheuristic optimization techniques.