摘要
针对配煤过程中最优方案失效的情况,提出了一种能够具有多备选方案的综合性能指标配煤优化方法.该方法采用一种新型的群智能优化算法——多元优化算法进行优化,算法中的搜索元采用上三角的数据结构存储,并利用该结构实现有用信息记忆和共享,充分利用寻优过程信息,实现搜索过程记忆,在找到全局最优解的同时,保留多个次优解.以某火电厂为工程应用实例研究,将该算法与其他5个常用的群智能优化算法的优化结果进行比较验证,结果表明多元优化算法在配煤优化中能够提供多种备选方案且最优解精度更高.采用多元优化算法进行具有多备选方案的配煤优化是可行有效的.多备选方案的综合性能指标配煤优化研究对火电企业具有重要的现实意义.
According to failure of the optimum because of varying environment, a method of various alterna- tives for coal blending optimization is proposed.A novel swarm intelligence algorithm-multivariant optimization al- gorithm the method being adopted is applied.The search atoms of the algorithm were stored in a upper triangular data structure form that was providing memory and information sharing.Then the search atoms made full use of optimization process information, and implemented process memorise. At last the global optimal solution could be found meanwhile various alternatives retention.A case study was performed by taking a thermal power plant as an engineering background. By comparing the optimization results of the algorithm with other five swam intelligence algorithms, it was proved that multivariant optimization algorithm provided various alternatives and had the advantages in the solutions＇ quality for coal blending optimization.It was effective to coal blending optimization using multivariant optimization algorithm.The method of various alternatives for coal blending optimization with integrated performance index has important practical significance for thermal power plants.
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第6期853-858,共6页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金(61261007)
关键词
多备选方案
配煤优化
多元优化算法
搜索元
过程记忆
various alternatives
coal blending optimization
multivariant optimization algorithm
search atom
process memorise
