Title :
Dynamic cloning based Immune Network algorithm for multi-modal optimization
Author :
Shi Xu-hua ; Zhu Yu-guang
Author_Institution :
Res. Inst. of Electr. Autom. Control, NingBo Univ., Ningbo, China
Abstract :
In this paper, a novel multi-modal optimization algorithm, namely Dcopt-aiNet is proposed, which is based on biological immune network mechanism for global numerical optimization. Different from de Castro´s opt-aiNet algorithm, Dcopt-aiNet models cloning operation using dynamic cloning operation which is adopted from biological immune network mechanism. Based on the multi-modal benchmarks, experiments were carried out to compare the performance of Dcopt-aiNet with that of opt-aiNet. Experiment results show that when compared with the opt-aiNet method, the new algorithm is capable of improving search performance significantly in successful rate and convergence speed.
Keywords :
artificial immune systems; Dcopt-aiNet; biological immune network; dynamic cloning operation; global numerical optimization; immune network algorithm; multimodal benchmark; multimodal optimization; opt-aiNet algorithm; Benchmark testing; Cloning; Convergence; Heuristic algorithms; Immune system; Optimization; artificial immune algorithms; dynamic cloning; multi-modal optimization;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022027