递减步长果蝇优化算法及应用

1)广州军区76127部队,湖南 郴州 424202; 2)军械工程学院,石家庄 050003

人工智能; 优化算法; 果蝇算法; 局部最优; 递减步长; 支持向量机; 回归模型

Research on and application of diminishing step fruit fly optimization algorithm
Ning Jianping1, Wang Bing1, 2, Li Hongru2, and Xu Baohua2

Ning Jianping1, Wang Bing1, 2, Li Hongru2, and Xu Baohua21)76127 Unit, Guangzhou Military Area, Chenzhou 424202, Hunan Province, P.R.China2)Ordnance Engineering College, Shijiazhuang 050003, P.R.China

artificial intelligence; optimization algorithm; fruit fly algorithm; global optimiation; diminishing step; support vector machine(SVM); regression model

DOI: 10.3724/SP.J.1249.2014.04367

备注

提出一种递减步长果蝇优化算法(diminishing step fruit fly optimization algorithm,DS-FOA).该算法的搜索步长随果蝇觅食进程逐步减小,从而使果蝇群体在觅食初期具有较强的全局搜索能力,在觅食后期具有较强的局部寻优能力,从而实现全局搜索能力和局部寻优能力的平衡.将该算法用于支持向量机(support vector machine,SVM)回归模型的惩罚因子和核函数参数优化中,结果表明,DS-FOA收敛速度快,全局搜索与局部寻优能力强.与其他算法相比,由DS-FOA优化参数的SVM回归模型均方误差最低,回归效果好.

A diminishing step fruit fly optimization algorithm(DS-FOA)is proposed.The step length is decreased progressively along with the process of foraging.DS-FOA demostrates preferable global optimization capability in early stage and local optimization capability in later period.Dynamic balance is achieved between global and local optimizing capability.Also DS-FOA is applied in the field of support vector machine(SVM)regression model parameter optimization.Experimental results show that the DS-FOA has fast convergence speed and powerful global and local optimization capability.The SVM model using DS-FOA has the lowest error of mean square and the best optimization result.

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