.png){kind=icon}
返回
Self-adaptive learning based immune algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11771-012-1105-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned problems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms (CLONALG), and the mean error index of each test function in SALIA decreases by a factor of at least 1.0x10(7) in average.
Keyword:
immune algorithm
multi-modal optimization
evolutionary computation
immune secondary response
self-adaptive learning
期刊
IF:
4.4
论文数:
5.2K
被引数:
1.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏
分享收藏
查看更多内容