Hybrid anti-prematuration optimization algorithm

Qiaoling Wang, Xiaozhi Gao, Changhong Wang*, Furong Liu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)503-508
Number of pages6
JournalJournal of Systems Engineering and Electronics
Issue number3
Publication statusPublished - Jun 2010
MoE publication typeA1 Journal article-refereed


  • Anti-prematuration
  • Artificial immune system (AIS)
  • Clonal selection
  • Hybrid optimization algorithm
  • Particle swarm optimization (PSO)


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