| 混合微分进化算法应用于换热网络优化 |
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| 引用本文： | 包艳冰,崔国民,陈家星,肖 媛.混合微分进化算法应用于换热网络优化[J].热能动力工程,2017,32(12):14-20. |
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| 作者姓名： | 包艳冰 崔国民 陈家星 肖 媛 |
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| 作者单位： | 上海理工大学 新能源科学与工程研究所，上海 200093,上海理工大学 新能源科学与工程研究所，上海 200093,上海理工大学 新能源科学与工程研究所，上海 200093,上海理工大学 新能源科学与工程研究所，上海 200093 |
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| 基金项目： | 上海市科委部分地方院校能力建设计划（16060502600）; 国家自然科学基金(51176125);沪江基金研究基地专项（D14001） |
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| 摘 要： | 针对微分进化(Differential Evolution,DE)算法应用于换热网络优化存在局部搜索能力不足、收敛速度慢和求解精度低等问题,提出一种混合微分进化(Hybrid Differential Evolution,HDE)算法。当DE算法的变异、交叉和选择操作不再使种群的最优值继续进化时,加入梯度操作使当前种群的最优个体趋向更好的解。为了防止算法早熟收敛,当种群的多样性低于设定的阈值时,引入迁移操作,在最优个体附近区域重新生成新的个体并以此替换旧的个体,增强算法的种群多样性。通过算例验证了该算法可以有效适用于换热网络的优化过程,具有更强的局部搜索能力,更快的收敛速度和更高的优化效率。
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| 关 键 词： | 关键词：微分进化算法 换热网络 局部搜索 梯度方法 种群多样性 |
A Hybrid Differential Evolution Algorithm Applied to Heat Exchanger Network Synthesis |
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| BAO Yan-bing,CUI Guo-min,CHEN Jia-xing and XIAO Yuan.A Hybrid Differential Evolution Algorithm Applied to Heat Exchanger Network Synthesis[J].Journal of Engineering for Thermal Energy and Power,2017,32(12):14-20. |
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| Authors: | BAO Yan-bing CUI Guo-min CHEN Jia-xing and XIAO Yuan |
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| Affiliation: | Research Institute of New energy Science and Technology, University of Shanghai for Science and Technology, Shanghai, China, Post Code:200093,Research Institute of New energy Science and Technology, University of Shanghai for Science and Technology, Shanghai, China, Post Code:200093,Research Institute of New energy Science and Technology, University of Shanghai for Science and Technology, Shanghai, China, Post Code:200093 and Research Institute of New energy Science and Technology, University of Shanghai for Science and Technology, Shanghai, China, Post Code:200093 |
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| Abstract: | Because the differential evolution (DE) algorithm is characterized by earlier mature and slow convergence in the later stage of evolution when applied in the optimization of heat exchanger network (HEN).A hybrid differential evolution (HDE) algorithm is put forward to apply in the synthesis of HEN. While the best individual of the current population cannot be generated any longer by mutation and crossover, the gradient method is applied to push the best individuals tends to be a better solution. In order to avoid the problem of premature convergence, a migrating operation is embedded into the DE algorithm when the population diversity fails to match the desired tolerance. The new individual is regenerated based on the best individual and replace it which will maintain the population diversity. Two cases are used to verify the feasibility of the algorithm. The results show that HDE algorithm is effective to improve local search ability, speed up convergence, heighten optimization efficiency.Key words |
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| Keywords: | differential evolution algorithm heat exchanger network local search gradient method population diversity |
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