A novel random walk algorithm with compulsive evolution for global optimization of heat exchanger networks

XIAO Yuan; CUI Guomin; LI Shuailong

doi:10.11949/j.issn.0438-1157.20160498

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A novel random walk algorithm with compulsive evolution for global optimization of heat exchanger networks

更新时间：2025-11-26

- A novel random walk algorithm with compulsive evolution for global optimization of heat exchanger networks
- CIESC Journal Issue 12, Pages: 5140-5147(2016)
- 作者机构：
  
  上海理工大学新能源科学与工程研究所,上海,200093
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20160498
  CLC： P18
- Online First：05 December 2016，
  
  Published：05 December 2016
- 稿件说明：
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XIAO Yuan, CUI Guomin, LI Shuailong. A novel random walk algorithm with compulsive evolution for global optimization of heat exchanger networks[J]. CIESC Journal, 2016, (12): 5140-5147.
DOI：

XIAO Yuan, CUI Guomin, LI Shuailong. A novel random walk algorithm with compulsive evolution for global optimization of heat exchanger networks[J]. CIESC Journal, 2016, (12): 5140-5147. DOI： 10.11949/j.issn.0438-1157.20160498.

摘要

应用启发式方法在换热网络全局优化上的优点，提出了一种全新的强制进化随机游走算法（random walk algorithm with compulsive evolution

RWCE），算法以目标函数减小为强制方向，通过各换热单元面积的随机扩大或缩小，同时实现了整型变量（换热单元数）和连续变量（换热单元面积）的同步优化。另外，算法能够以一定的概率选择接受差解，使其具备极强的跳出局部最优解的能力和全局搜索能力。算例验证表明，RWCE算法相比于其他启发式方法具有程序简单、更易实现、算法适应性及全局搜索能力更强的优点，使优化质量得到进一步提升。

Abstract

A novel random walk algorithm with compulsive evolution(RWCE) was proposed on the basis of different heuristic methods for global optimization of heat exchanger networks. In RWCE algorithm

both integer(e.g.

number of heat exchanger units) and continuous(e.g.

area of heat exchanger) variables were optimized simultaneously by expanding or contracting randomly area of heat exchangers in the direction of targeting cost reduction. Moreover

when individuals walked around local optima

the RWCE algorithm could compulsively accept imperfect networks at certain probability such that it had strong capability of jumping out of the local optima and continuing global optimization. Several case studies indicated that the proposed RWCE algorithm

compared to other heuristic methods

possessed characteristics of simple evolution strategy

strong algorithm suitability and global searchability

which significantly improved optimization performance.

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