Haopeng Zhang
Research interests:
Control theory and optimization:
dynamical systems, distributed control, multi-agent systems, numerical optimization and swarm intelligence
Applications:
Power systems, swarm robots, glucose insulin regulation
Education
- Ph.D. in Mechanical Enginnering, Texas Tech University, 2014
- B.S. in Electrical Engineering, Sun Yat-Sen Univeristy, 2009
Publications
Many-Objective optimization problems (MaOPs) are the optimization problems which contain more than three conflicting objectives. Extensive interests from both algorithms development and practical applications are attracted to study the MaOPs. The success of the Particle Swarm Optimization (PSO) algorithm and Evolutionary Algorithm (EA) as single-objective optimizers motivated researchers to extend the use of those techniques to solve the MaOPs: many-objective particle swarm optimization algorithms (MOPSOs) and many-objective evolutionary algorithms (MOEAs). In this paper, we extend a recently developed bio-inspired optimization algorithm, Multi-agent Coordination Optimization Algorithm (MCO) from a single-objective optimizer to a many-objective optimizer: Many-Objective Multi-agent Coordination Optimization Algorithm (MOMCO)