File Transfer Scheduling Optimization Using Artificial Immune System

Natural immune system has features such as pattern recognition, diversity, learning, distributed detection, stochastic detection, and adaptability, that make it a great metaphor to solve hard problems. In two last decades artificial immune systems, as a novel computational artificial intelligence approach, are used to solve hard optimization problems. As for existence of different theories and models in theoretical immunity society, researchers have created various algorithms to simulate processes of immune system, such as immune network based models, negative selection, clonal selection. In this paper a clonal selection algorithm have been used to solve File Transfer Scheduling optimization problem. In proposed approach, antibodies have been created such that, the degree of simultaneous sending of files be maximized for a given transfer sequence of files, this cause make-span of schedule be minimized for that sequence. The proposed algorithm have been examined on this problem with different size, the result of experiments shown that, reaching global optimum rate of the algorithm are acceptable. Also a population control strategy is used to reduce the run time of algorithm.