Multiobjective optimization using an immunodominance and clonal selection inspired algorithm
Multiobjective optimization using an immunodominance and clonal selection inspired algorithm
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DOI：
10.1007/s11432-008-0040-2
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Maoguo Gong;L. Jiao;Wenping Ma;Haifeng Du
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文献类型：
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作者：
Maoguo Gong;L. Jiao;Wenping Ma;Haifeng Du
基于免疫优势机制和克隆选择理论，提出了一种新的多目标优化算法--免疫优势克隆多目标算法(IDCMA)。IDCMA的独特之处在于，它将当前优势个体的适应值指定为迄今为止已发现的优势个体与其中一个非优势个体之间的自定义距离度量的值，称为抗体-抗体亲和力。根据抗体亲和力的大小，所有占优势的个体(抗体)可分为亚显性抗体和隐匿性抗体。此外，局部搜索只适用于次优势抗体，而隐蔽抗体在局部搜索过程中是冗余的，不起作用，但在随后的进化过程中可以成为次优势(主动)抗体。此外，还提供了一种新的免疫操作，克隆增殖，以增强局部搜索。IDCMA通过克隆增殖操作来复制个体，并在局部搜索后选择其改进的成熟后代，从而使单个个体能够有效地利用其周围的空间，使新手获得更广阔的搜索空间。IDCMA与MISA、NSGA-II、SPEA、PAES、NSGA、VEGA、NPGA和HLGA在求解6个著名的多目标函数优化问题和9个多目标0/1背包问题上的性能比较表明，IDCMA具有良好的收敛性能和良好的分布特性。
Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performan ce comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.