摘要
将利用贝叶斯网络构造的系统故障概率函数作为可靠性指标,考虑费用、质量、体积构造了资源约束函数。针对微粒群算法引斥力规则的不足,提出了搜索后期斥力增强型混合引斥力微粒群算法(LRPSO算法):在搜索前期,使微粒在其他微粒的引斥力作用下进行最优搜索,以保持种群多样性;在搜索后期,减小引力、增强斥力,利用斥力项避免微粒陷入较差位置,以提高局部搜索能力。算法测试和可靠性优化实例验证了LRPSO算法的有效性。
Fault probability function constructed by Bayesian network was regarded as reliability index,and the resource constraint functions were established by considering the functions of cost,weight and volume.To overcome the shortages of attraction and repulsion rule of particle swarm optimization algorithm,a LRPSO algorithm was proposed.At the earlier-stage,each particle searched the optimum under the attraction and repulsion produced by all particles,to maintain the population diversity.At the later-stage,the effect of attraction was reduced and the effect of repulsion was enhanced using the repulsion term to avoid particles being trapped in worse searching position and improving local searching ability.The effectiveness of LRPSO algorithm was verified by algorithm tests and reliability optimization examples.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2014年第21期2930-2936,共7页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51405426
50905154)
河北省自然科学基金资助项目(E2012203015)
河北省教育厅资助科研项目(ZH2012062)
关键词
可靠性优化
贝叶斯网络
引斥力
LRPSO算法
reliability optimization
Bayesian network
attraction and repulsion
later-stage repulsion-enhanced hybrid attraction and repulsion particle swarm optimization（LRPSO）algorithm
