| ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE |
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| 引用本文： | 罗印升 李人厚 张维玺. ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE[J]. 西安交通大学学报(英文版), 2005, 17(2): 117-121 |
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| 作者姓名： | 罗印升 李人厚 张维玺 |
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| 作者单位： | School of Electronics and Information Engineering, Xi＇an Jiaotong University, Xi＇an 710049, China |
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| 摘 要： | Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group＇s artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed.
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| 关 键 词： | 人工智能 最优化设计 SGA 神经免疫系统 |
| 文章编号： | 1671-8267（2005）02-0117-05 |
ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE |
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| Luo Yinsheng,Li Renhou,Zhang Weixi. ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE[J]. Academic Journal of Xi’an Jiaotong University, 2005, 17(2): 117-121 |
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| Authors: | Luo Yinsheng Li Renhou Zhang Weixi |
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| Abstract: | Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group's artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed. |
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| Keywords: | germinal center reaction B cell artificial immune algorithm multi-modal function |
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