| 基于人工蜂群算法的改进研究与实现 |
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| 引用本文： | 廉德胜,徐晓钟,孙璐.基于人工蜂群算法的改进研究与实现[J].上海师范大学学报(自然科学版),2017,46(2):200-205. |
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| 作者姓名： | 廉德胜 徐晓钟 孙璐 |
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| 作者单位： | 上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234 |
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| 摘 要： | 参数的选择直接影响着最小二乘支持向量机(LSSVM)的泛化性能和回归效验,是确保LSSVM优秀性能的关键.为了解决以上问题,对人工蜂群算法(ABC)进行了改进,引入新解越界处理方法,研究了一种基于双种群策略的蜂群算法,同时提出提出一种运行时参数调整方法,然后验证优化后的算法IIABC的准确性与健壮性.燃气回归分析采用平均绝对百分比误差(MAPE)作为IIABC算法基准方法,实验结果表明基于IIABC-LSSVM预测结果比IABC-LSSVM有着更高的准确性.
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| 关 键 词： | 最小二乘支持向量机 人工蜂群算法 稳健性 平均绝对百分比误差 |
| 收稿时间： | 2015/9/9 0:00:00 |
Improvement and implementation of algorithm based on artificial bee colony |
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| Lian Desheng,Xu Xiaozhong and Sun Lu.Improvement and implementation of algorithm based on artificial bee colony[J].Journal of Shanghai Normal University(Natural Sciences),2017,46(2):200-205. |
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| Authors: | Lian Desheng Xu Xiaozhong and Sun Lu |
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| Institution: | College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China,College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China and College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China |
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| Abstract: | Selection of the hyper-parameters is critical to the performance of Least Squares Support Vector Machines (LSSVM),directly impacting the generalization and regression efficacy of the LSSVM.In order to solve the problem above,this paper based on ABC has done certain researches and improvement (IIABC) which have been applied to the LSSVM regression analysis.In this paper,the Artificial Bee Colony (ABC) Algorithm is improved,introducing a new cross processing method,studying a ABC based on double population policy,and putting forward a run-time parameter adjustment method,and then the robustness and accuracy of the optimized IIABC are verified.Experiment adopts MAPE as the benchmark IIABC algorithm for gas regression analysis,and shows that forecasting based on the IIABC-LSSVM is of higher accuracy than the IABC-LSSVM. |
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| Keywords: | LSSVM ABC robustness MAPE |
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