化工学报2017，Vol.68Issue(8)：3161-3167,7.DOI:10.11949/j.issn.0438-1157.20161786
一种求解过程动态优化问题的生物地理学习粒子群算法
Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes
摘要
Abstract
Intelligent optimization algorithms have been playing an increasing role in dynamic optimization, due to advantages of wide applicability and strong global searching capability. Biogeography-based learning particle swarm optimization (BLPSO) was proposed for dynamic optimization problems (DOPs) by hybridizing biogeography-based and particle swarm optimization. BLPSO employed a new biogeography-based learning approach for construction of learning examples by ranking of particles (i.e., the quality of particles) and dimension as unit, such that learning efficiency was enhanced. Control vector parameterization first converted DOPs into nonlinear programming problems which were then solved by BLPSO. The simulation results on typical DOPs with non-differentiable, multi-modal and multi-variable characteristics show that BLPSO has outstanding solution precision and convergence speed.关键词
全局优化/动态学/算法/控制向量参数化/生物地理学习粒子群算法Key words
global optimization/dynamics/algorithm/control vector parameterization/biogeography-based learning particle swarm optimization分类
化学化工引用本文复制引用
陈旭,梅从立,徐斌,丁煜函,刘国海..一种求解过程动态优化问题的生物地理学习粒子群算法[J].化工学报,2017,68(8):3161-3167,7.基金项目
江苏省自然科学基金项目(BK20160540,BK20130531) （BK20160540,BK20130531）
江苏大学人才启动基金项目(15JDG139) （15JDG139）
中国博士后科学基金项目(2016M591783) （2016M591783）
中央高校基本科研业务费重点科研基地创新基金项目(222201717006).supported by Natural Science Foundation of Jiangsu Province (BK20160540, BK20130531), the Research Talents Startup Foundation of Jiangsu University (15JDG139), China Postdoctoral Science Foundation (2016M591783) and the Fundamental Research Funds for the Central Universities (222201717006). （222201717006）
