Improved aquila optimizer for swarm-based solutions to complex engineering problems.

Area of Science:

• Artificial Intelligence
• Computational Optimization
• Meta-heuristic Algorithms

Background:

• Traditional optimization methods often face challenges such as local optima, slow convergence, and inefficient search in unknown spaces.
• Existing single-solution approaches limit effectiveness and productivity in complex problem-solving.

Purpose of the Study:

• To introduce an Improved Aquila Optimizer (IAO), a novel meta-heuristic algorithm inspired by the hunting strategies of the Aquila.
• To enhance optimization capabilities, effectiveness, and productivity compared to existing methods.

Main Methods:

• The IAO algorithm simulates Aquila's hunting process, incorporating distinct phases: low flight with leisurely descent (exploitation), high-altitude dives and contour flying (exploration), and swooping maneuvers (capture).
• The IAO's performance was evaluated using 23 classical optimization functions and five real-world engineering problems.
• Comparative analysis included convergence curves, time complexity, and Wilcoxon ranksum tests.

Main Results:

• The IAO demonstrated superior performance against various champion algorithms on classical optimization functions.
• The algorithm proved effective across diverse application domains when applied to real-world engineering challenges.
• Time complexity analysis revealed a best time of 0.00015225, outperforming other algorithms, with a p-value < 0.05 from Wilcoxon ranksum test, indicating statistical significance.

Conclusions:

• The Improved Aquila Optimizer (IAO) is a resilient and adaptable tool for addressing challenging optimization issues.
• IAO exhibits significant efficacy and competitiveness, positioning it as a valuable optimization tool for real-world engineering applications.