摘要
为优化篦冷机控制参数,提高换热效率,将传热和粘性耗散引起的修正熵产数分别作为目标函数,利用遗传算法对篦冷机参数进行多目标优化.为增加多目标遗传算法的种群多样性,提高算法的局部搜索能力,对传统的非支配排序精英遗传算法(NSGA-Ⅱ)进行部分功能改进.构建多种群、多交叉算子的操作模式,根据子种群对最优解集的贡献量自适应调节子种群规模,利用局部搜索算法提高算法的局部搜索能力.通过标准多目标优化问题验证所提出算法的有效性,并根据优化得到的篦冷机熵产数的最优解集,给出冷却风机功率最小的最优控制方案,通过与生产线的实际数据进行对比验证其优化效果.
To optimize the grate cooler control parameters and improve the heat exchange efficiency,the modified entropy production numbers caused by heat transfer and viscous dissipation are respectively taken as objective functions,and the genetic algorithm is used for grate cooler parameters multi-objective optimization.In order to increase the population diversity of the multi-objective genetic algorithms and improve the local search ability of the algorithms,some improvements are made to non-dominated sorting genetic algorithm(NSGA-Ⅱ).The operation modes of multi-group and multi-crossing operators are constructed.According to the contribution of sub-populations to the optimal solution set the size of the sub-populations size is adaptively adjusted.The local search algorithm is used to improve the local search ability of the algorithm.The effectiveness of the proposed algorithm is verified by some benchmark multi-objective optimization problems.According to the optimal solution set,the optimal control scheme for the minimum cooling fan power is given.The comparison with the actual data of the production line verifies the optimization effect.
作者
赵志彪
刘浩然
刘彬
闻言
ZHAO Zhi-biao;LIU Hao-ran;LIU Bin;WEN Yan(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;School of Mechanical Engineering,Yan)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第5期1217-1225,共9页
Control and Decision
基金
国家自然科学基金项目(51641609)
河北省自然科学基金项目(F2019203320,E2018203398)。
关键词
篦冷机
熵
热力学
多目标优化
NSGA-Ⅱ
grate cooler
entropy
thermodynamics
multi-objective optimization
NSGA-Ⅱ
