化学工程2024，Vol.52Issue(4)：40-45,57,7.DOI:10.3969/j.issn.1005-9954.2024.04.008
一种自适应强制进化随机游走算法应用于换热网络综合
An adaptive random walk algorithm with compulsive evolution algorithm for heat exchange network synthesis
摘要
Abstract
When the RWCE(random walk algorithm with compulsive evolution)algorithm was applied to energy system optimization,the maximum step length affected both the range of the current feasible search domain and the evolution of integer variables.The fixed parameter setting further reduced the probability of optimal solutions.Therefore the RWCE algorithm that integrated adaptive step size and opposition-based learning strategy was proposed.A stochastic dynamic step size was established to automatically motivate the beneficial step size valued to evolve continuously under the traction of the guiding parameter.On the basis,the individual evolution path was changed by the adaptive opposition-based learning,so that the algorithm could automatically search for the better step size at different stages of optimization and explore as many structures as possible,so as to give full play to the global search and local exploitation capability of the algorithm.Finally,three typical medium-to-large scale cases H6C10,H10C10 and H13C7 were studied and evaluated.The results show that the proposed method can further improve the algorithm's search capability.关键词
自适应/导向参数/反向学习/换热网络/RWCEKey words
adaptive method/guiding parameter/opposition-based learning/heat exchanger networks/RWCE分类
能源科技引用本文复制引用
段欢欢,易智康,张笑恬,肖媛,崔国民..一种自适应强制进化随机游走算法应用于换热网络综合[J].化学工程,2024,52(4):40-45,57,7.基金项目
国家自然科学基金资助项目(21978171,51976126) （21978171,51976126）
中国博士后科学基金资助项目(2020T13043) （2020T13043）
