Journal of Systems Engineering and Electronics ›› 2010, Vol. 21 ›› Issue (3): 503-508.doi: 10.3969/j.issn.1004-4132.2010.03.023

• CONTROL THEORY AND APPLICATION • Previous Articles     Next Articles

Hybrid anti-prematuration optimization algorithm

Qiaoling Wang1, Xiaozhi Gao2, Changhong Wang1,*, and Furong Liu1   

  1. 1. Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, P. R. China;
    2. Department of Electrical Engineering, Helsinki University of Technology, Otakaari 5 A, Espoo 02150, Finland
  • Online:2010-06-23 Published:2010-01-03


Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems. This paper presents a hybrid optimization technique combining two heuristic optimization methods, artificial immune system (AIS) and particle swarm optimization (PSO), together in searching for the global optima of nonlinear functions. The proposed algorithm, namely hybrid anti-prematuration optimization method, contains four significant operators, i.e. swarm operator, cloning operator, suppression operator, and receptor editing operator. The swarm operator is inspired by the particle swarm intelligence, and the clone operator, suppression operator, and receptor editing operator are gleaned by the artificial immune system. The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate. It is also employed to cope with a real-world optimization problem.