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Raindrop Optimizer: A Novel Nature-inspired Metaheuristic Algorithm For Artificial Intelligence And Engineering Optimization

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Raindrop Optimizer: A Novel Nature-inspired Metaheuristic Algorithm For Artificial Intelligence And Engineering Optimization

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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

Shengjin Chen^1,2, Guangyong Yang^3,4, Guanghai Cui^1,2

¹School of Electrical and Information Engineering, Yunnan Minzu University, Kunming, 650500, Yunnan, China.

Scientific Reports

|October 1, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A new Raindrop Algorithm (RD) offers efficient optimization for AI tasks. This novel meta-heuristic method excels in complex engineering problems, achieving superior results in benchmark tests and robotic applications.

Keywords:

Artificial intelligence Constrained optimization Heuristic algorithm Metaheuristic algorithm Raindrop algorithm (RD)

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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.