一种采用种群多样性监控和实时更新策略的粒子群优化算法

计算物理 ›› 2017, Vol. 34 ›› Issue (3): 344-354.

一种采用种群多样性监控和实时更新策略的粒子群优化算法

李帅龙, 崔国民, 陈家星, 肖媛

上海理工大学, 新能源科学与工程研究所, 上海 200093

收稿日期:2016-04-06 修回日期:2016-07-06 出版日期:2017-05-25 发布日期:2017-05-25
作者简介:李帅龙(1991-),男,硕士研究生,主要从事过程系统优化研究,E-mail:shuailong_li@163.com
基金资助:
国家自然科学基金（51176125）、上海市科委部分地方院校能力建设计划（16060502600）及沪江基金研究基地专项（D14001）资助项目

An Improved Particle Swarm Optimization Based on Diversity Monitor and Real-time Updating Strategy

LI Shuailong, CUI Guomin, CHEN Jiaxing, XIAO Yuan

Institute of New Energy Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2016-04-06 Revised:2016-07-06 Online:2017-05-25 Published:2017-05-25

摘要/Abstract

摘要： 针对粒子群算法优化后期容易出现早熟收敛问题，建立一种具有种群多样性监测和实时更新策略的改进方法.首先建立种群健康度指标用来评价粒子群进化状态；其次提出随机扰动策略和离心搜索策略用于丰富粒子群的种群多样性，增强算法的全局搜索能力，并提出梯度搜索策略用于精确、高效地搜寻当前邻域内的局部极值点，提高算法的计算效率.最后建立种群健康度反馈机制，使粒子可以实时感知种群的健康程度，并自适应地采用不同的粒子更新策略，保证粒子群处于健康进化水平.将新方法应用于优化实例，并与其它改进方法进行性能比较，结果验证了新方法的有效性.

关键词: 换热网络, 粒子群算法, 种群多样性, 局部搜索

Abstract: Particle swarm optimization (PSO) algorithm has strong ability to explore global optimal region for heat exchanger networks synthesis. However, particles may trap into local optima and converge prematurely in late evolution. Therefore, an improved particle swarm optimization algorithm based on diversity feedback and real-time updating strategy is proposed. Firstly, index of population health degree is established to evaluate population diversity during evolution. Secondly, a random perturbation strategy and a centrifugal strategy are combined respectively with PSO algorithm to enrich population diversity and enhance global search ability. Furthermore, gradient search strategy is applied to search efficiently local optima and improve computational efficiency of PSO algorithm. Finally, a feedback mechanism of population health degree is proposed to real-time monitor health status of population and further to adopt different update strategies for keeping particles healthy during evolution. The method was applied to several cases taken from literature and results are encouraging. They are better than those of other improvements for PSO.

Key words: heat exchanger networks, particle swarm optimization, population diversity, local search

中图分类号:

TK124

李帅龙, 崔国民, 陈家星, 肖媛. 一种采用种群多样性监控和实时更新策略的粒子群优化算法[J]. 计算物理, 2017, 34(3): 344-354.

LI Shuailong, CUI Guomin, CHEN Jiaxing, XIAO Yuan. An Improved Particle Swarm Optimization Based on Diversity Monitor and Real-time Updating Strategy[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2017, 34(3): 344-354.

参考文献

[1] MASSO A,RUDD D. The synthesis of system designs. Ⅱ. Heuristic structuring[J].AIChE Journal,1969,15(1):10-17.
[2] LINNHOFF B, HINDMARSH E. The pinch design method for heat exchanger networks[J].Chemical Engineering Science,1983,38(5):745-763.
[3] TRIVEDI K K,O'NEILL B K,ROACH J R,et al. A new dual-temperature design method for the synthesis of heat exchanger networks[J].Computers & Chemical Engineering,1989,13(6):667-685.
[4] FRASER D M. The use of minimum flux instead of minimum approach temperature as a design specification for heat exchanger networks[J].Chemical Engineering Science,1989,44(5):1121-1127.
[5] TRIVEDI K K,O'NEILL B K,ROACH J R,et al. Systematic energy relaxation in MER heat exchanger networks[J].Computers & Chemical Engineering,1990,14(6):601-611.
[6] YERRAMSETTY K M,MURTY C V S. Synthesis of cost-optimal heat exchanger networks using differential evolution[J].Computers & Chemical Engineering,2008,32(8):1861-1876.
[7] LEWIN D R,WANG H,SHALEV O. A generalized method for HEN synthesis using stochastic optimization-I. General framework and MER optimal synthesis[J].Computers & Chemical Engineering,1998,22(10):1503-1513.
[8] LEWIN D R. A generalized method for HEN synthesis using stochastic optimization-Ⅱ.:The synthesis of cost-optimal networks[J].Computers & Chemical Engineering,1998,22(10):1387-1405.
[9] LIN B,MILLER D C. Solving heat exchanger network synthesis problems with Tabu Search[J].Computers & Chemical Engineering,2004,28(8):1451-1464.
[10] PARIYANI A, GUPTA A,GHOSH P. Design of heat exchanger networks using randomized algorithm[J].Computers & Chemical Engineering,2006,30(6/7):1046-1053.
[11] SILVA A P,RAVAGNANI M A S S,BISCAIA E C,et al. Optimal heat exchanger network synthesis using particle swarm optimization[J].Optimization and Engineering,2009,11(3):459-470.
[12] KENNEDY J,EBERHART R. Particle swarm optimization[C]//Neural Networks, 1995. Proceedings, IEEE International Conference on, 1995:1942-1948.
[13] FURMAN K C,SAHINIDIS N V. Computational complexity of heat exchanger network synthesis[J].Computers & Chemical Engineering,2001,25(9/10):1371-1390.
[14] 夏涛,贾涛,程杰. 群智优化算法同步综合换热网络[J].北京化工大学学报(自然科学版),2009(1):97-101.
[15] 严丽娣,霍兆义,尹洪超. 粒子群算法最优同步综合换热网络[J].化工进展,2009,28(S1):439-442.
[16] HUO Z,ZHAO L, YIN H,et al. A hybrid optimization strategy for simultaneous synthesis of heat exchanger network[J].Korean Journal of Chemical Engineering,2012,29(10):1298-1309.
[17] 何巧乐,崔国民,许海珠. 文化基因粒子群算法在换热网络连续变量全局优化中的应用[J].石油化工,2014,(1):37-45.
[18] 肖媛,崔国民,彭富裕,等. 粒子群算法在非线性系统应用中的早熟现象及其改进[J].计算物理,2015,32(6):693-700.
[19] YEE T F,GROSSMAN I E. Simultaneous optimization models for heat integration. Ⅱ. Heat exchanger network synthesis[J].Computers & Chemical Engineering,1990,14(10):1165-1184.
[20] PENG F,CUI G. Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm[J].Applied Thermal Engineering,2015,78:136-149.
[21] RAVAGNANI M A S S,SILVA A P,ARRYO P A,et al. Heat exchanger network synthesis and optimisation using genetic algorithm[J].Applied Thermal Engineering,2005,25(7):1003-1017.