Optimizing constrained non-convex NLP problems in chemical engineering field by a novel modified goal programming genetic algorithm

Authors:
Cuiwen Cao;Jinwei Gu;Bin Jiao;Zhong Xin;Xingsheng Gu
Affiliations:
East China University of Science and Technology, Shanghai, China;East China University of Science and Technology, Shanghai, China;Shanghai Dianji University, Shanghai, China;East China University of Science and Technology, Shanghai, China;East China University of Science and Technology, Shanghai, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

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.