文献类型：会议论文

英文题名：Optimizing Constrained Non-convex NLP Problems in Chemical Engineering Field by a Novel Modified Goal Programming Genetic Algorithm

作者：Cao, Cuiwen[1];Gu, Jinwei[1];Jiao, Bin;Xin, Zhong[1];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Tech, Shanghai, Peoples R China

会议论文集：World Summit on Genetic and Evolutionary Computation (GEC 09)

会议日期：JUN 12-14, 2009

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Non-convex NLP; equality constraints; MGPGA

摘要：A novel modified goal programming genetic algorithm (MGPGA) is presented in this paper to solve constrained non-convex nonlinear programming (NLP) problems. This new method eliminates the complex equality constraints from original model and transforms them as parts of goal functions with higher priority weighting factors. At the same time, the original objective function has the lowest priority weighting factor. After all the absolute deviations of these equality constraints objectives are minimized, the final optimized solutions can be gained. Some applications in chemical engineering field are tested by this MGPGA. The proposed MGPGA demonstrates its advantages in better performances and abilities of solving non-convex NLP problems especially for those with equality constraints.