Optimization of distillation resources based on neighborhood-clonal selection learning algorithm

doi:10.3969/j.issn.0438-1157.2012.09.023

CIESC Journal ›› 2012, Vol. 63 ›› Issue (9): 2818-2823.DOI: 10.3969/j.issn.0438-1157.2012.09.023

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Optimization of distillation resources based on neighborhood-clonal selection learning algorithm

YANG Zhong

School of Information Science and Engineering, Ningbo University, Ningbo 315211, Zhejiang, China

Received:2012-06-14 Revised:2012-06-20 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the Public Science and Technology Research Funds Projects of Zhejiang Province(2011C21077),Ningbo Natural Science Foundation(2011A610173)and Ningbo Key Construction Service-oriented Professionals(Sfwxzdzy200903).

基于邻域-克隆选择学习算法的分馏装置负荷优化

杨忠, 史旭华

宁波大学信息科学与工程学院, 浙江宁波 315211

通讯作者: 史旭华
作者简介:杨忠( 1990-),男,本科。
基金资助:
浙江省公益科技项目(2011C21077);浙江省自然科学基金项目(Y1090548);宁波市自然科学基金项目(2011A610173);宁波市服务型重点建设专业项目(Sfwxzdzy200903)。

Abstract

Abstract: A neighbourhood-clonal selection learning algorithm(N-Clonalg)is proposed in this paper,which is combined with the idea of biological immune clonal selection system and multi-agent technology. Different from other artificial immune algorithms,N-Clonalg is based on the grid environment,and contains three main search operators,i.e.,N-clonal selection,N-competition and self-learning operators.Combining global and local searching operations,N-Clonalg overcomes the phenomena of precocious and slow convergence,and can better achieve the global optimal solutions effectively in the individual space,which is proved in multi-modal benchmarks.Distillation resources optimization shows that it has better search performance.

Key words: clonal selection learning, neighborhood-clonal selection learning algorithm, multi-modal optimization, distillation column

摘要： 在免疫克隆选择和人工免疫网络算法基础上,采用了Agent的思想,提出了一种邻域-克隆选择学习全局优化算法(N-Clonalg)。不同于其他人工免疫算法,N-Clonalg定义了网格化的邻域操作环境,其主要搜索算子有N-克隆选择、N-竞争和自学习算子,能有机结合全局与局部搜索,多峰测试函数表明能较好地克服克隆选择算法(Clonalg)的早熟及人工免疫网络算法(Opt-aiNet)收敛速度慢问题。分馏装置负荷优化实例应用表明,算法具有较好的最优解搜索性能,能较好地实现化工中的寻优问题。

关键词: 克隆选择学习, 邻域-克隆选择学习算法, 多模态优化, 分馏装置

CLC Number:

TP391

YANG Zhong. Optimization of distillation resources based on neighborhood-clonal selection learning algorithm[J]. CIESC Journal, 2012, 63(9): 2818-2823.

杨忠, 史旭华. 基于邻域-克隆选择学习算法的分馏装置负荷优化[J]. 化工学报, 2012, 63(9): 2818-2823.

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Optimization of distillation resources based on neighborhood-clonal selection learning algorithm

基于邻域-克隆选择学习算法的分馏装置负荷优化

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