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Multi-modal topological optimization of structure using immune algorithm

机译：基于免疫算法的结构多峰拓扑优化

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摘要

In this paper the authors describe a novel approach MMIA (multi-modal immune algorithm) for rinding optimal solutions to multi-modal structural problems emulating the features of a biological immune system. The use of an immune algorithm as opposed to a genetic algorithm provides this methodology with superior local search ability. Inter-relationships within the proposed algorithm 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 MMIA in order to improve the balance between exploitation and exploration. Moreover the concept of cytokines is applied for constraint handling. Two well-studied benchmark examples in structural topology optimization problems were used to evaluate the proposed approach. The results indicate the effectiveness of MMIA.
机译：在本文中，作者描述了一种新颖的方法MMIA（多模式免疫算法），用于为模拟生物免疫系统特征的多模式结构问题注入最佳解决方案。与遗传算法相反，免疫算法的使用为该方法提供了出色的局部搜索能力。在特异性，生发中心和适应性免疫反应的记忆特性方面，所提出算法中的相互关系类似于抗体-抗原关系。基因片段重组和几种抗体多样化方案（包括体重组，体突变，基因转化，基因回复，基因漂移和核苷酸添加）被纳入MMIA，以改善开发与探索之间的平衡。此外，细胞因子的概念被应用于约束处理。在结构拓扑优化问题中，两个经过充分研究的基准示例用于评估该方法。结果表明了MMIA的有效性。

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