Fast random opposition-based learning Aquila optimization algorithm.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Meta-heuristic algorithms are crucial for solving complex optimization problems.
• The Aquila Optimization (AO) algorithm, a swarm-based method, faces challenges like slow convergence and local optima.
• Developing efficient meta-heuristic algorithms remains an active research area.

Purpose of the Study:

• To introduce a novel hybrid algorithm, FROBLAO, by integrating Fast Random Opposition-Based Learning (FROBL) with the Aquila Optimization (AO) algorithm.
• To enhance the convergence speed and global search capability of the AO algorithm.
• To address the limitations of the standard AO algorithm in complex optimization tasks.

Main Methods:

• The proposed FROBLAO algorithm combines the hunting strategy of Aquila birds with a Fast Random Opposition-Based Learning mechanism.
• Performance evaluation using standard test suites: CEC 2005, CEC 2019, and CEC 2020.
• Validation on six real-world engineering optimization problems.
• Statistical analysis including Wilcoxon rank-sum, t-test, and Friedman test to compare FROBLAO with other algorithms.

Main Results:

• FROBLAO demonstrated superior performance compared to the standard AO algorithm and other competing methods.
• The algorithm showed improved convergence rates and a reduced tendency to get trapped in local optima.
• Statistical tests confirmed the significant effectiveness of FROBLAO across various optimization benchmarks.
• Successful application to real-life engineering problems, highlighting its practical utility.

Conclusions:

• The FROBLAO algorithm effectively overcomes the limitations of the standard AO algorithm, particularly in complex optimization scenarios.
• The integration of FROBL significantly enhances the optimization process, leading to better solutions.
• FROBLAO represents a promising advancement in swarm-based meta-heuristic optimization techniques.