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Immune response-based algorithm for optimization of dynamic environments

Immune response-based algorithm for optimization of dynamic environments
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摘要 A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle.The dynamic process of artificial immune response with operators such as immune cloning,multi-scale variation and gradient-based diversity was modeled.Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens,a sigmoid model that can clearly describe clonal proliferation was proposed.In addition,with the introduction of multiple populations and multi-scale variation,the algorithm can well maintain the population diversity during the dynamic searching process.Unlike traditional artificial immune algorithms,which require randomly generated cells added to the current population to explore its fitness landscape,AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments.Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks.Preliminary experiments show that AIDE can maintain high population diversity during the search process,simultaneously can speed up the optimization.Thus,AIDE is useful for the optimization of dynamic environments. A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle. The dynamic process of artificial immune response with operators such as immune cloning, multi-scale variation and gradient-based diversity was modeled. Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens, a sigmoid model that can clearly describe clonal proliferation was proposed. In addition, with the introduction of multiple populations and multi-scale variation, the algorithm can well maintain the population diversity during the dynamic searching process. Unlike traditional artificial immune algorithms, which require randomly generated cells added to the current population to explore its fitness landscape, AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments. Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks. Preliminary experiments show that AIDE can maintain high population diversity during the search process, simultaneously can speed up the optimization. Thus, AIDE is useful for the optimization of dynamic environments.
作者 史旭华 钱锋
出处 《Journal of Central South University》 SCIE EI CAS 2011年第5期1563-1571,共9页 中南大学学报(英文版)
基金 Project(60625302) supported by the National Natural Science Foundation for Distinguished Young Scholars of China Project(2009CB320603) supported by the National Basic Research Program of China Projects(10dz1121900,10JC1403400) supported by Shanghai Key Technologies R & D Program Project supported by the Fundamental Research Funds for the Central Universities in China Project(200802511011) supported by the New Teacher Program of Specialized Research Fund for the Doctoral Program of Higher Education in China Project(Y1090548) supported by Zhejiang Provincial Natural Science Fund,China Project(2011C21077) supported by Zhejiang Technology Programme,China Project(2011A610173) supported by Ningbo Natural Science Fund,China
关键词 dynamic optimization artificial immune algorithms immune response multi-scale variation 优化算法 免疫反应 环境 基础 人工免疫算法 搜索过程 多样性 反应原理
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