Dynamic artificial immune system and its application to File Transfer Scheduling optimization

Milad Dastan Zand, M. Kalantari, S. Golzari
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Abstract

There are different theories and models in natural immune system, so computer science researchers have created various algorithms to simulate processes of immune system, such as immune network based models, negative selection, and clonal selection. In this paper a novel dynamic clonal selection algorithm has been used to solve File Transfer Scheduling optimization problem. In proposed algorithm, the parameters of clonal selection algorithm will be changed over generations with hope of decreasing run-time, and at the same time the performance of the algorithm remains at an acceptable level. Then after some generations a population control strategy handles the size of antibody population. Antibodies have been created such that, the degree of simultaneous sending of files be maximized for a given transfer sequence of files. This causes make-span of schedule be minimized for that sequence. The proposed algorithm has been examined on these problems with different sizes. The results of experiments show that, the rate of reaching to global optimum is acceptable.
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动态人工免疫系统及其在文件传输调度优化中的应用
自然免疫系统有不同的理论和模型,因此计算机科学研究人员创建了各种算法来模拟免疫系统的过程,如基于免疫网络的模型、负选择和克隆选择。本文提出了一种新的动态克隆选择算法来解决文件传输调度优化问题。在该算法中,克隆选择算法的参数会随着代的变化而变化,以期减少运行时间,同时算法的性能保持在可接受的水平。然后在几代之后,种群控制策略处理抗体种群的大小。抗体已经被创建,使得同时发送文件的程度在给定的文件传输序列中被最大化。这将使该序列的进度跨度最小化。该算法已在不同规模的问题上进行了验证。实验结果表明,达到全局最优的速度是可以接受的。
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