Clonal Selection with Immune Dominance and Anergy Based Multiobjective Optimization
Clonal Selection with Immune Dominance and Anergy Based Multiobjective Optimization
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DOI：
10.1007/978-3-540-31880-4_33
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发表时间：
2005-03
影响因子：
4.1
通讯作者：
L. Jiao;Maoguo Gong;Ronghua Shang;Haifeng Du;Bin Lu
中科院分区：
文献类型：
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作者：
L. Jiao;Maoguo Gong;Ronghua Shang;Haifeng Du;Bin Lu
基于免疫优势的概念和抗体克隆选择理论，提出了一种新的人工免疫系统算法-免疫优势克隆多目标算法（IDCMA）。对主要参数的影响进行了实证分析。通过与随机权重遗传算法和强度Pareto进化算法的仿真比较表明，对于低维多目标问题，IDCMA在两个集合的间隔和覆盖等指标上具有最优性能。
Based on the concept of Immunodominance and Antibody Clonal Selection Theory, we propose a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA). The influences of main parameters are analyzed empirically. The simulation comparisons among IDCMA, the Random-Weight Genetic Algorithm and the Strength Pareto Evolutionary Algorithm show that when low-dimensional multiobjective problems are concerned, IDCMA has the best performance in metrics such as Spacing and Coverage of Two Sets.