Variable Selection Method Based on Extreme Ant Colony Optimization for Pulverizing Capability Model

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Variable Selection Method Based on Extreme Ant Colony Optimization for Pulverizing Capability Model

更新时间：2025-07-09

- Variable Selection Method Based on Extreme Ant Colony Optimization for Pulverizing Capability Model
- Vol. 45, Issue 10, Pages: 7-12(2011)
- 作者机构：
  
  西安交通大学电力设备电气绝缘国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：10 October 2011，
  
  Published：2011
- 稿件说明：
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Variable Selection Method Based on Extreme Ant Colony Optimization for Pulverizing Capability Model[J]. 2011, 45(10): 7-12.
DOI：

Variable Selection Method Based on Extreme Ant Colony Optimization for Pulverizing Capability Model[J]. 2011, 45(10): 7-12. DOI：

摘要

针对制粉出力难以直接测量

以及制粉系统包含变量多且各变量间耦合性强的问题

提出了一种利用极值蚁群优化的制粉出力建模变量选择法

并采用支持向量机根据所选变量建立了制粉出力的预测模型.该算法基于蚁群优化的正反馈原理

对蚂蚁搜索到的各个变量的相对重要性加以区分

并根据幂律分布选择重要性较小的变量进行变异

使得较差解不断得到改善

从而引导蚂蚁朝着最优解的方向搜索.采用制粉系统现场数据对所提算法、蚁群算法和蚁群遗传算法进行比较

结果表明

所提算法具有更快的收敛速度

且由其所选变量建立的制粉出力模型具有较高的预测精度.

Abstract

Since the pulverizing capability is unable to be measured directly and there exist numerous strongly-coupling variables in a ball mill pulverizing system

a variable selection method based on extreme ant colony optimization(EACO)for pulverizing capability model is proposed. According to the selected variables

the model is established by support vector regression. On the basis of the positive feedback mechanism of ant colony optimization(ACO)

the proposed method is able to identify the relative importance of variables selected by ants

and then mutate unimportant variables following the power law distribution

thus the poor solutions in each iteration process get improved. Consequently

the ants are led to search for the optimal solutions. The proposed algorithm is compared with ACO and ant colony optimization with genetic algorithm on the field data of ball mill pulverizing system. The experiment results show the higher convergence rate and prediction accuracy.

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references

刘定平,肖蔚然,陆继东. 中间储仓式制粉系统调整的优化研究[J]. 华北电力大学学报, 2007, 34(3): 55-59.

LIU Dingping,XIAO Weiran,LU Jidong. Research on optimizing for adjustment of storage pulverizing system[J]. Journal of North China Electric Power University,2007,34(3): 55-59.

程启明,王勇浩. 火电厂中间仓储式球磨机制粉系统控制技术发展综述[J]. 上海电力学院学报, 2006, 22(1): 48-54.

CHENG Qiming,WANG Yonghao. The overview on the development of control techniques on intermediate storage bunker ball mill pulverizing system of power plant[J]. Journal of Shanghai University of Electric Power, 2006, 22(1): 48-54.

WANG Heng, JIA Mingping, HUANG Peng, et al. A study on a new algorithm to optimize ball mill system based on modeling and GA[J]. Energy Conversion and Management, 2010, 51(4): 846-860.

苏志刚,王培红,于向军,等. 中储式制粉系统出力在线监测软测量建模[J]. 中国电机工程学报, 2007, 27(29): 90-99.

SU Zhigang, WANG Peihong, YU Xiangjun, et al. Soft sensor modeling for on-line monitoring the capacity of coal pulverizing system[J]. Proceedings of the CSEE, 2007, 27(29): 90-99.

DORIGO M, STÜTZLE T. Ant colony optimization[M]. Cambridge,MA,USA: MIT Press, 2004:25-63.

MOHAMMAD G, MATHEUS P F, RICHARD J. Ant colony optimization as a feature selection method in the QSAR modeling of anti-HIV-1 activities of 3-(3,5-dimethylbenzyl)uracil derivatives using MLR, PLS and SVM regressions[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 98(2): 123-129.

SHAHLA N, MOHAMMAD B. Text-independent speaker verification using ant colony optimization-based selected features[J]. Expert Systems with Applications, 2011, 38(1): 620-630.

MOHAMMAD E B, SHAHLA N. A novel hybrid ACO-GA algorithm for text feature selection[C]∥Proceedings of IEEE Congress on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 2009: 2561-2568.

NEMATI S, BASIRI M, AGHAEE N, et al. A novel ACO-GA hybrid algorithm for feature selection in protein function prediction [J]. Expert Systems with Application, 2009, 36(10): 12086-12094.

BOETTCHER S, PERCUS A G. Optimization with extremal dynamics[J]. Complexity, 2003, 8(2): 57-62.

冯瑞,宋春林,张艳珠,等. 基于支持向量机与RBF神经网络的软测量模型比较研究[J]. 上海交通大学学报, 2003, 37(S1): 122-125.

FENG Rui, SONG Chunlin, ZHANG Yanzhu, et al. Comparative study of soft sensor models based on support vector machines and RBF neural networks[J]. Journal of Shanghai Jiaotong University, 2003, 37(S1): 122-125.

陈朱娟,陈绍炳. 基于一种新的灰关联系数表达式的磨煤出力关联分析[J]. 发电设备, 2009, 23(6): 393-396.

CHEN Zhujuan, CHEN Shaobing. Coal mill capacity analysis based on a new expression of gray relation coefficient[J]. Power Equipment, 2009,23(6):393-396.

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