Moia: Multi-objective immune algorithm

被引：77

作者：

Luh, GC ^{[1
]}

Chueh, CH ^{[1
]}

Liu, WW ^{[1
]}

机构：

[1] Tatung Univ, Dept Mech Engn, Taipei, Taiwan

来源：

ENGINEERING OPTIMIZATION | 2003年 / 35卷 / 02期

关键词：

biological immune system; multi-objective immune algorithm; Pareto optimal solutions; strength Pareto evolutionary algorithm;

D O I：

10.1080/0305215031000091578

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：143 / 164

页数：22

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