摘要
智能优化算法具有适用性广泛、全局搜索能力强等优点,近年来在动态优化中的应用逐渐增多。通过混合生物地理优化与粒子群优化,提出了生物地理学习粒子群(biogeography-based learning particle swarm optimization,BLPSO)算法,并用于动态优化问题的求解。BLPSO采用了新型的生物地理学习方式,该方式根据粒子"排名",即粒子的优劣,以维度为单位构造学习粒子,提高了学习的效率。针对动态优化问题,首先通过控制向量参数化将其转化为非线性规划问题,然后采用BLPSO算法进行求解。最后,将BLPSO应用于非可微、多峰、多变量等典型动态优化问题的求解,计算结果表明BLPSO具有较好的搜索精度和收敛速度。
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 panicle 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 convened 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.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2017年第8期3161-3167,共7页
CIESC Journal
基金
江苏省自然科学基金项目(BK20160540
BK20130531)
江苏大学人才启动基金项目(15JDG139)
中国博士后科学基金项目(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)~~
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
global optimization
dynamics
algorithm
control vector parameterization
biogeography-basedlearning particle swarm optimization
