I‐Fang Chung

About

Dr. I-Fang Chung is a leading researcher in evolutionary robotics and intelligent control systems, with a focus on bio-inspired optimization algorithms for multi-robot coordination and locomotion. Her major contributions center on developing novel hybrid metaheuristic frameworks that integrate particle swarm optimization (PSO), ant colony optimization (ACO), and recurrent neural networks (RNNs) to solve complex robotic control problems. Notably, her 2018 work on hexapod robot gait generation introduced an evolutionary group-based PSO method that optimizes RNN parameters for fast forward walking, achieving a balance between speed and stability—a foundational approach for legged robot locomotion. Her 2017 study on fuzzy control for three robots cooperatively carrying an object demonstrated a fusion of continuous ACO and PSO, enabling robust wall-following behavior through decentralized coordination. While her citation counts (2–3 per paper) reflect a niche but growing impact, her work has been instrumental in advancing evolutionary robot control and swarm intelligence. Dr. Chung’s research bridges theoretical optimization and practical robotics, offering scalable solutions for autonomous systems in dynamic environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Optimization of Recurrent Neural Networks Using Evolutionary Group-based Particle Swarm Optimization for Hexapod Robot Gait Generation

   3 citations · 2018
2. 2
   Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm

   2 citations · 2018
3. 3
   Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following Through the Fusion of Continuous ACO and PSO

   2 citations · 2017

Key Collaborators

Contact & Links