Enhancing the Water Lily Effect Algorithm Using a Fuzzy Inference System

The Water Lily Effect Algorithm, introduced in 2024, is inspired by the processes of pollination and movement on water lily leaves. It utilizes concepts of static and dynamic swarm intelligence, modeled through dragonfly movements, to enhance the algorithm's exploratory capabilities. Additionally, local pollination and water movement on lily leaves contribute to its extraction efficiency. However, the algorithm lacks a precise mechanism for managing key parameters during exploration and extraction, with dragonfly movements being randomly defined in all scenarios. This limitation reduces both the accuracy and convergence speed of the optimization process. This study proposes integrating a fuzzy inference system into the dragonfly movement mechanism to address these issues. By controlling the neighborhood radius, alignment, and cohesion movements, the algorithm achieves improved performance. When tested on 12 high-dimensional benchmark functions (50 dimensions), the fuzzy-enhanced Water Lily Algorithm showed a more than 49% improvement in convergence accuracy and over a 9% increase in convergence speed compared to the original algorithm.