Enhanced Aquila optimizer based on tent chaotic mapping and new rules.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Metaheuristic algorithms are vital for complex problem-solving, offering simplicity and robust optimization.
• The Aquila Optimizer (AO) is effective but can suffer from slow convergence and local optima issues.

Purpose of the Study:

• To introduce an enhanced Aquila Optimizer, the Tent-enhanced Aquila Optimizer (TEAO), to overcome limitations of the standard AO.
• To improve population initialization and balance exploration-exploitation for accelerated and precise optimization.

Main Methods:

• TEAO integrates the Tent chaotic map for improved initial population distribution.
• Novel formulas are introduced to enhance the balance between exploration and exploitation phases.
• The algorithm's performance is evaluated using 23 benchmark functions and six constrained engineering problems.

Main Results:

• TEAO demonstrated superior performance compared to 14 state-of-the-art algorithms across benchmark functions.
• The algorithm achieved better solution quality and stability when applied to constrained engineering problems.
• Experimental results consistently show TEAO's advantage over existing advanced optimization techniques.

Conclusions:

• The Tent-enhanced Aquila Optimizer (TEAO) effectively addresses the convergence speed and local optima issues of the standard Aquila Optimizer.
• TEAO offers a more competitive and robust solution for various optimization tasks, including complex engineering problems.
• The proposed enhancements provide a significant advancement in metaheuristic algorithm design.