针对果蝇优化算法在寻优过程中易陷入局部最优、寻优结果对算法参数的选取依赖性较强而导致结果不稳定等现象，在研究相关理论的基础上从算法的结构上对算法性能进行分析，通过基准函数的测试研究了影响算法性能的主要因素，将云模型相关理论引入算法改进，从算法的寻优步长和最优解产生机制两方面对算法进行优化改进.在算法的嗅觉搜索阶段引入味道浓度影响因子，由味道浓度影响因子自适应动态调整算法的搜索步长，提高算法的全局搜索能力和局部寻优能力；在计算味道浓度阶段引入正态云模型，利用正态云发生器以果蝇个体到原点的距离为期望生成正态云，体现算法中果蝇个体味道浓度的随机性和模糊性，改进算法最优解的产生机制，提高算法的寻优精度.最后将改进后的算法应用于自动组卷系统，建立基于云模型果蝇优化算法的自动组卷数学模型，通过实验验证了算法在组卷效率和组卷精度上都具有较好的效果.
Abstract
In order to overcome the demerits of Fruit Fly Optimization Algorithm(FOA),such as easily relapsing into local optimum and unstable results which are caused by strong dependence on the selection of algorithm parameters,On the basis of studying the related theories,the performance of the Fruit Fly Optimization Algorithm is analyzed from the algorithm structure and the main factors affecting the performance of the algorithm are studied by benchmark function test.The cloud model theory is introduced into the algorithm improvement,and the algorithm is optimized and improved from two aspects:the optimal step size of the algorithm and the optimal solution generation mechanism.Firstly,the conception of taste concentration introduced and adjusted adaptively for controlling search step to improve the global search ability and local optimization ability of the algorithm.Then,the randomness and fuzziness of smell concentration parameter is described by normal cloud model and adjusted to finish osphresis search operation automatically to improve the searching precision of the algorithm.Finally,The improved algorithm is used to the automatic test,compared and analyzed with the experiment of other FOA in reference literatures.The results of experiment show that the improved algorithm has better advantages of test efficiency and accuracy.
关键词
果蝇优化算法 /
正态云模型 /
自动组卷
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Key words
Fruit fly optimization algorithm /
Normal cloud model /
Automatic test
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