| 基于改进参数的粒子群算法的换热网络优化 |
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| 引用本文： | 周静,崔国民,彭富裕,肖媛.基于改进参数的粒子群算法的换热网络优化[J].能源研究与信息,2019,35(2):106-109,116. |
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| 作者姓名： | 周静 崔国民 彭富裕 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： | 国家自然科学基金资助项目（51176125）；上海市研究生创新基金项目（JWCXSL1301） |
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| 摘 要： | 对于换热网络综合优化问题,粒子群算法能有效解决其容易陷入局部最优和无法收敛到全局最优的局限性。标准粒子群算法具有较强的随机性,可调节参数较少,不同的参数配置对算法的优化效果有显著影响。在分析粒子群算法中各参数特点的基础上,通过合理调整参数,使该算法在连续变量优化过程中具有更好的全局收敛性能。采用4股流体的小规模换热网络算例进行验证,优化结果表明,改进参数后的粒子群算法对计算换热网络综合问题有效。
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| 关 键 词： | 换热网络综合 粒子群算法 参数改进 全局收敛 |
| 收稿时间： | 2016/9/16 0:00:00 |
Optimization of heat exchange network based on particle swarm with improved parameters |
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| ZHOU Jing,CUI Guomin,PENG Fuyu and XIAO Yuan.Optimization of heat exchange network based on particle swarm with improved parameters[J].Energy Research and Information,2019,35(2):106-109,116. |
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| Authors: | ZHOU Jing CUI Guomin PENG Fuyu and XIAO Yuan |
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| Affiliation: | Institute of New Energy Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,Institute of New Energy Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,Institute of New Energy Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China and Institute of New Energy Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | For the synthetic optimization of heat exchange network, particle swarm optimization (PSO) can effectively solve the problems of easily falling into local optimum and limitations of non-convergence to global optimum. Standard PSO has strong randomness and less adjustable parameters. Different parameter configurations have a significant effect on the optimization. Based on the analysis of the PSO parameter characteristics, better global convergence of this algorithm could be achieved in the process of continuous variable optimization by the adjustment of the parameter values in this paper. The optimization results showed that the improved PSO was effective for the synthetic optimization of heat exchange network when verified using a 4-stream example. |
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| Keywords: | heat exchanger network synthesis particle swarm optimization parameter improvement global convergence |
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