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引用次数: 12

摘要

这是一种新的优化算法,它模仿蚊子的行为来寻找蚊帐上的洞,如果有的话。蚊子的飞行和滑动运动都被建模并纳入算法中。对不同类型的不同维数和模态的基准函数进行了全局最小值测试。即Gramacy & Lee, Ackley, Rastrigin, Rosenbrock, Griewank和Schwefel的一,二,五,十和三十维函数。实验中,每个功能和维度随机生成30个不同的种子。结果表明,该算法具有高效、收敛和准确的特点。
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Mosquito flying optimization (MFO)
This is a new optimization algorithm which mimic the behavior of mosquito to find a hole in mosquito net, if any. Both the flying and sliding motion of the mosquito have been modelled and incorporated in the algorithm. The algorithm was tested for global minima on different type of benchmark functions of various dimension and modality. Namely Gramacy & Lee, Ackley, Rastrigin, Rosenbrock, Griewank and Schwefel functions of one, two, five, ten and thirty dimensions. The experiment was done with thirty different seeds generated randomly for each function and dimension. The algorithm was found to be efficient, convergent and accurate.
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