Raindrop optimizer: a novel nature-inspired metaheuristic algorithm for artificial intelligence and engineering...

Area of Science:

• Artificial Intelligence
• Optimization Algorithms
• Computational Intelligence

Background:

• Meta-heuristic optimization methods are crucial for solving complex problems in AI.
• Existing algorithms often face challenges with convergence speed and local optima.
• Nature-inspired algorithms offer novel approaches to optimization.

Purpose of the Study:

• Introduce a novel meta-heuristic optimization algorithm, the Raindrop Algorithm (RD).
• Evaluate the performance of the RD algorithm on benchmark functions and real-world engineering problems.
• Demonstrate the algorithm's effectiveness in improving state estimation and controller parameters in robotics.

Main Methods:

• The Raindrop Algorithm (RD) employs distinct exploration (splash, diversion, evaporation) and exploitation (convergence, overflow) phases.
• Algorithm performance was validated using 23 benchmark functions and the CEC-BC-2020 benchmark suite.
• RD was applied to optimize state estimation filters and controller parameters in robotic engineering.

Main Results:

• The RD algorithm demonstrated rapid convergence, reaching optimal solutions typically within 500 iterations.
• Achieved first-place rankings in 76% of test cases across benchmark functions.
• Significantly outperformed conventional methods in robotic applications, reducing position estimation error by 18.5% and improving filtering accuracy by 7.1%.

Conclusions:

• The Raindrop Algorithm (RD) is a computationally efficient and highly competitive optimization method.
• RD shows statistically significant superiority and consistent top-three performance in complex engineering optimization tasks.
• RD presents a promising solution for challenging optimization problems in AI-driven engineering environments.