Endocrine-Immune Network and Its Application for Optimization
Endocrine-Immune Network and Its Application for Optimization
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DOI：
10.1007/978-3-642-37105-9_17
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Hao Jiang;Tundong Liu;J. Chen;Jiping Tao
中科院分区：
文献类型：
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作者：
Hao Jiang;Tundong Liu;J. Chen;Jiping Tao
提出了一种基于内分泌系统调节的新型人工免疫网络模型（EINET）。在这个用于优化的EINET中，采用或修改了多个算子，旨在更快的收敛速度和更好的最优解。进一步来说，一种新的操作符——激素调节，受内分泌系统启发，发挥双向调节机制，根据激素更新功能进行消除和突变，以增加抗体群体的多样性。而抗体学习是个体通过学习免疫网络中的记忆抗体而进行的进化。然后，利用称为酶反应的局部搜索过程来促进搜索空间的利用并加速收敛。为了评估所提出的模型是否可以直接扩展到解决组合优化问题的有效算法，设计了EINET-TSP算法。使用TSPLIB中的一些基准实例进行对比实验，与现有的应用于组合优化问题的免疫网络相比，结果表明EINET-TSP算法能够在解质量上显着提高搜索性能。
A novel artificial immune network model (EINET) based on the regulation of endocrine system is proposed. In this EINET for optimization, several operators are employed or revised which aim at faster convergence speed and better optimal solution. Further speaking, a new operator, hormonal regulation, exerts a bidirectional regulatory mechanism inspired from endocrine system, which undergoes elimination and mutation according to hormone updating function, to increase the diversity of antibody population. And antibody learning is an evolution of individuals through learning from memory antibody in immune network. Then, a local search procedure called enzymatic reaction is utilized to facilitate the exploitation of the search space and speed up the convergence. To evaluate whether the proposed model can be directly extended to an effective algorithm for solving combinatorial optimization problem, EINET-TSP algorithm is designed. Comparative experiments are conducted using some benchmark instances from the TSPLIB, and the results compared with the existing immune network applied to combinatorial optimization problem shows that the EINET-TSP algorithm is capable of improving search performance significantly in solution quality.