MOIA: Multi-objective immune algorithm
MOIA: Multi-objective immune algorithm
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DOI：
10.1080/0305215031000091578
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发表时间：
2003-04
影响因子：
2.7
通讯作者：
G. Luh;C. Chueh;Wei‐Wen Liu
中科院分区：
文献类型：
--
作者：
G. Luh;C. Chueh;Wei‐Wen Liu
该论文描述了一种基于生物免疫系统特征寻找多目标优化问题帕累托最优解的新算法。所提出的多目标免疫算法（MOIA）内的相互关系在特异性、生发中心和适应性免疫反应的记忆特征方面类似于抗体-抗原关系。 MOIA 中纳入了基因片段重组和多种抗体多样化方案（包括体细胞重组、体细胞突变、基因转换、基因回复、基因漂移和核苷酸添加），以改善开发和探索之间的平衡。使用五个性能指标，将 MOIA 模拟数据与源自强度帕累托进化算法 (SPEA) 的数据进行比较。结果表明 MOIA 在几个方面优于 SPEA。
The paper describes a novel algorithm for finding Pareto optimal solutions to multi-objective optimization problems based on the features of a biological immune system. Inter-relationships within the proposed multi-objective immune algorithm (MOIA) resemble antibody-antigen relationships in terms of specificity, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes (including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition) were incorporated into the MOIA in order to improve the balance between exploitation and exploration. Using five performance metrics, MOIA simulation figures were compared with data derived from a strength Pareto evolutionary algorithm (SPEA). The results indicate that the MOIA outperformed the SPEA in several areas.