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

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

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

Full text

Pdf (488 KB)

Source

ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents

Shanghai, China

SESSION: Full papers table of contents

Pages 17-24

Year of Publication: 2009

ISBN:978-1-60558-326-6

Authors

Cuiwen Cao	East China University of Science and Technology, Shanghai, China
Jinwei Gu	East China University of Science and Technology, Shanghai, China
Bin Jiao	Shanghai Dianji University, Shanghai, China
Zhong Xin	East China University of Science and Technology, Shanghai, China
Xingsheng Gu	East China University of Science and Technology, Shanghai, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 11, Downloads (12 Months): 35, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1543834.1543838 What is a DOI?

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Ryoo, H.S. and Sahinidis, N.V. 1995. Global optimization of nonconvex NLPs and MINLPs with applications in process design. Comput. Chem. Eng. 19, 5, 551--566.
	2	Boading Liu , Broading Liu, Theory and Practice of Uncertain Programming, Physica-Verlag, 2002
	3	Kocis, G.R. and Grossmann, I.E. 1987. Relaxation strategy for the structural optimization of process flow sheets. Ind. Eng. Chem. Res. 26, 9, 1869--1880.
	4	Kocis, G.R. and Grossmann, I.E. 1988. Global optimization of nonconvex mixed-integer nonlinear programming (MINLP) problems in process synthesis. Ind. Eng. Chem. Res. 27, 1407--1421.
	5	Kocis, G.R. and Grossmann, I.E. 1989. A modeling and decomposition strategy for the MINLP optimization of process flowsheets. Comput. Chem. Eng. 13, 10, 797--819.
	6	Floudas, C.A., Aggarwal, A., and Ciric, A.R. 1989. Global optimum search for non-convex NLP and MINLP problems. Comput. Chem. Eng. 13, 10, 1117--1132.
	7	Lorenz T. Biegler , Jorge Nocedal , Claudia Schmid , David Ternet, Numerical Experience with a Reduced Hessian Methodfor Large Scale Constrained Optimization, Computational Optimization and Applications, v.15 n.1, p.45-67, Jan. 2000 [doi>10.1023/A:1008723031056]
	8	Tapio, W. et al. 1998. An extended cutting plane method for a class of non-convex MINLP problems. Comput. Chem. Eng. 22, 3, 357--365.
	9	Cuiwen, C., Xingsheng, G. and Zhong X. 2009. Chance constrained programming models for refinery short-term crude oil scheduling problem, Applied Mathematical Modelling, 33, 3, 1696--1707.
	10	Androulakis, I.P. and Venkatasubramanian, V.A. 1991. Genetic algorithmic framework for process design and optimization. Comput. Chem. Eng. 15, 4, 217--228.
	11	Garrard, A. and Fraga, E.S. 1998. Mass exchange network synthesis using genetic algorithms. Comput. Chem. Eng. 22, 12, 1837--1850.
	12	Ku, H.M. and Karimi, I. 1991. An evaluation of simulated annealing for batch scheduling. Ind. Eng. Chem. Res. 30, 1, 163--169.
	13	Cardoso, M.F. et al. 2000. Optimization of reactive distillation processes with simulated annealing. Comput. Chem. Eng. 55, 5059--5078.
	14	Jayaraman, V.K. et al. 2001. Dynamic optimization of fed-batch bioreactors using the ant algorithm. Biotechnology Program. 17, 81--88.
	15	Zbigniew Michalewicz , Marc Schoenauer, Evolutionary algorithms for constrained parameter optimization problems, Evolutionary Computation, v.4 n.1, p.1-32, Spring 1996 [doi>10.1162/evco.1996.4.1.1]
	16	Yiqing, L., Xigang, Y. and Yongjian Liu. 2007. An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Comput. Chem. Eng. 31, 3, 153--162.
	17	Deb, K. 2000. An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 18, 311--318.
	18	Summanwar, V.S. et al. 2002. Solution of constrained optimization problems by multi-objective genetic algorithm. Comput. Chem. Eng. 26, 1481--1492.
	19	Surry, P.D. and Radcliffe, N.J. 1997. The COMOGA method: constrained optimization by multi-objective genetic algorithms. Control and Cybernetics. 26, 3, 391--412.
	20	Charnes, A. and Cooper, W.W. 1961. Management Models and Industrial Applications of Linear Programming. Wiley, New York.
	21	Liebman, J. et al. 1986. Modeling and optimization with GINO. The Science Press, Palo Alto, CA.
	22	Swaney, R.E. 1990. Global solution of algebraic nonlinear programs. AIChE Annl. Mtg., Chicago, IL.
	23	Willi Hock , K. Schittkowski, Test Examples for Nonlinear Programming Codes, Springer-Verlag New York, Inc., Secaucus, NJ, 1981
	24	Mezura-Montes, E., Coello Coello, C.A. 2005. A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation. 9, 1, 1--17.
	25	Venkatraman, S. and Yen, G.G. 2005. A genetic framework for constrained optimization using genetic algorithm. IEEE Transactions on Evolutionary Computation. 9, 4, 424--435.
	26	Slawomir Koziel , Zbigniew Michalewicz, Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization, Evolutionary Computation, v.7 n.1, p.19-44, Spring 1999 [doi>10.1162/evco.1999.7.1.19]
	27	Manousiouthakis, M. and Sourlas, D. 1992. A global optimization approach to rationally constrained rational programming. Comput. Chem. Eng. 115, 127--147.