蒸汽压缩系统的效率受其使用的翅片管换热器性能的强烈影响。热交换器的性能受到制冷剂回路的强烈影响，即管的连接顺序。本文描述了一种基于进化计算的方法来设计用于智能换热器设计系统（ISHE）的优化制冷剂回路。ISED中使用的技术采用两种不同的方法生成设计，即基于知识的进化计算模块和基于符号学习的进化计算模块。本文给出的优化示例分别使用了每个模块，并使用组合方法来展示每个模块的优点和组合模块方法的威力。通过这些优化运行确定的最佳电路设计比原始设计有了实质性的改进； 符号学习和基于知识的模块返回的电路设计分别使换热器的容量提高了2.6%和4.8%，而组合模块方法得到的电路设计使容量提高了6.5%。引文:ASHRAE会议论文，2010年，第116卷，pt。新墨西哥州阿尔伯克基2号

The efficiency of a vapor compression system is strongly influenced by the performance of the finned-tube heat exchangers it employs. Heat exchanger performance is strongly influenced by the refrigerant circuitry, i.e., the connection sequence of the tubes. This paper describes an evolutionary computation-based approach to designing an optimized refrigerant circuitry used in Intelligent System for Heat Exchanger Design (ISHED).The technique used in ISHED employs two separate approaches to generate designs, the knowledge-based evolutionary computation module and the symbolic learning-based evolutionary computation module. The optimization example presented in this paper employed each module independently and used the combined approach to demonstrate the benefits of each module and the power of the combined module approach. The best circuitry designs determined through these optimization runs yielded substantial improvements over the original design; the symbolic learning and knowledge based modules returned circuitry designs that improved the heat exchanger capacity by 2.6 % and 4.8 % respectively, while the combined module approach resulted in a circuitry design that improved the capacity by 6.5 %.