Luli Gao

Communication University of China, Southwest University

Papers

Total Citations

H-Index

About

Luli Gao is a researcher whose work bridges the frontiers of swarm intelligence and complex network control. Her primary research areas include bio-inspired optimization algorithms, mobile robot path planning, and data-driven synchronization for complex networks. Gao’s most notable contribution is the development of the Para-PSO-ABC algorithm (ParaPA), a novel swarm intelligence structure inspired by parasitic relationships in the biosphere. This algorithm, published in 2023, integrates modified particle swarm optimization with artificial bee colony methods to solve mobile robot path planning problems, earning 7 citations for its innovative multi-swarm evolutionary approach. In her earlier work, Gao tackled the challenging problem of data-driven optimal synchronization for complex networks with unknown dynamics. Her 2020 paper proposed a pre-compensation technique to construct an augmented error system, circumventing the need for explicit system dynamics—a significant achievement for controlling real-world networks where models are unavailable. Though her citation counts are still growing, Gao’s work demonstrates creative problem-solving by drawing inspiration from biological systems and applying rigorous control theory to practical robotics and network challenges.