Optimizing constrained non-convex NLP problems in chemical engineering field by a novel modified goal programming genetic algorithm
Pages 17 - 24
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.
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Index Terms
- Optimizing constrained non-convex NLP problems in chemical engineering field by a novel modified goal programming genetic algorithm
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Published In
June 2009
1112 pages
ISBN:9781605583266
DOI:10.1145/1543834
- General Chairs:
- Lihong Xu,
- Erik D. Goodman,
- Program Chairs:
- Guoliang Chen,
- Darrell Whitley,
- Yongsheng Ding
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Published: 12 June 2009
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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit
June 12 - 14, 2009
Shanghai, China
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