A multipurpose parallel genetic hybrid algorithm for non-linear non-convex programming problems

European Journal of Operational Research

Volumn 152, Issue 1, 2004, Pages 195-214

  Östermark, Ralf ^a  

^a ÅBO AKADEMI UNIVERSITY   (Finland)

Author keywords

Discontinuous functions; Evolutionary computation; Mixed integer non linear programming; Non convexity

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MATHEMATICAL PROGRAMMING; MONTE CARLO METHODS; NEURAL NETWORKS; VECTORS;

GENETIC HYBRID ALGORITHMS (GHA);

OPERATIONS RESEARCH;

EID: 0141636334     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00672-0     Document Type: Article

References (29)

1. Beaumont P.M., Bradshaw P.T. A distributed parallel genetic algorithm for solving optimal growth models. Computational Economics. 8:1995;159-179.
2. Dennis J.E., Schnabel R.B. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. 1983;Prentice-Hall, Englewood Cliffs, NJ.
3. Dorsey R.E., Mayer W.J. Genetic algorithms for estimation problems with multiple optima, nondifferentiability and other irregular features. Journal of Business and Economic Statistics. 13:1995;53-66.
4. Harjunkoski I., Westerlund T., Isaksson J., Skrifvars H. Different formulations for solving trim loss problem in a paper converting mill with ILP. Computers and Chemical Engineering. 20:1996;121-126.
5. FORTRAN Subroutines for Statistical Analysis. 1987;IMSL, USA.
6. Joines J., Houck C. On the use of non-stationary penalty functions to solve nonlinear constrained optimization functions with GAs. Proceedings of the Evolutionary Computation Conference. 1994;579-584 IEEE World Congress on Computational Intelligence, Orlando.
7. Lawrence, C., Zhou, J., Tits, A., 1997. User's Guide for CFSQP version 2.5: A Code for Solving (Large Scale) Constrained Nonlinear (Minimal) Optimization Problems, Generating Iterations Satisfying All Inequality Constraints. University of Maryland, College Park, MD 20472. Institute for Systems Research TR-94-16rl (Copyright by Craig T. Lawrence, Jian L. Zhou and Andreá L. Tits).
8. Michalewicz Z. Genetic. Algorithms + Data Structures = Evolution Programs:1993;Springer-Verlag, AI series, New York.
9. Michalewicz Z. Heuristic methods for evolutionary computation techniques. Journal of Heuristics. 1:1995;177-206.
10. Murtaugh, B.-A., Saunders M.A., 1998. MINOS 5.5 User's Guide. Technical Report SOL 83-20R. Department of Engineering Economic Systems and Operations Research, Stanford University, Stanford CA 94305.
11. Östermark R. Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm. Computational Economics. 13(2):1999;103-115.
12. Östermark R. Solving a nonlinear nonconvex trim loss problem with a genetic hybrid algorithm. Computers and Operations Research. 26:1999;623-635.
13. Östermark R. A flexible multicomputer algorithm for elementary matrix operations. Computers and Operations Research. 27:1999;245-268.
14. Östermark R. A hybrid genetic fuzzy neural network algorithm designed for classification problems involving several groups. Fuzzy Sets and Systems. 114(2):2000;311-324.
15. Östermark R. A flexible genetic hybrid algorithm for nonlinear mixed-integer programming problems. Evolutionary Optimization. 2000;. accepted for publication.
16. Östermark R. Genetic modelling of multivariate EGARCHX-processes. Evidence on the international asset return signal response mechanism. Computational Statistics and Data Analysis. 38(1):2001;1-124.
17. Östermark R. Automatic detection of parsimony in heteroskedastic time series processes. Empirical tests on global asset returns with parallel geno-mathematical programming. Soft Computing. 6(1):2002;45-63.
18. Östermark R., Saarinen M. A multiprocessor interior point algorithm, kybernetes. Studies in Systems and Cybernetics. 25(4):1996;84-100. Anniversary issue.
19. Östermark R., Westerlund T., Skrifvars H. A nonlinear mixed-integer multiperiod firm model. International Journal of Production Economics. 67:2000;188-199.
20. Pettersson, F., 1994. Mixed Integer Non-Linear Programming Applied on Pump Configurations. Dissertation. Process Design Laboratory, Faculty of Chemical Engineering, Åbo Akademi University, Åbo, Finland.
21. Pörn R., Harjunkoski I., Westerlund T. Convexification of different classes of non-convex MINLP problems. Computers and Chemical Engineering. 1999.
22. Press W.H., Flannery B.P., Teukolsky S.A., Vetterling W.T. Numerical Recipes in PASCAL. The Art of Scientific Computing. 1989;Cambridge University Press, Cambridge.
23. Richardson J., Palmer M., Liepins G., Hilliard M. Some guidelines for genetic algorithms with penalty functions. Proceedings of the Third International Conference on Genetic Algorithms. 1989;191-192 Morgan Kaufmann, Los Altos, CA.
24. Saxén, H., Saxén, B., 1994. A tool for modeling simulation and predicting using feed-forward and recursive neural networks. In: XIV Congresso de Sociedade Brasiliena de Computacao. Caxambu, MG, Brazil, August 1994, Anaois, pp. 55-60.
25. Scales L.E. Introduction to Non-linear Optimization. 1985;Macmillan Computer Science Series, London.
26. Schittkowski, K., 1991. Solving Nonlinear Programming Problems with Very Many Constraints, Matematisches Institut, Universität Bayreuth, Report No. 294, Bayreuth, Germany.
27. Siedlicki W., Sklanski J. Constrained genetic optimization via dynamic reward-penalty balancing and its use in pattern recognition. Proceedings of The Third International Conference on Genetic Algorithms. 1989;141-150 Morgan Kaufmann, Los Altos, CA.
28. Westerlund T., Pettersson F., Grossmann I.E. Optimization of pump configurations as a MINLP problem. Computers and Chemical Engineering. 18(9):1994;845-858.
29. Westerlund T., Skrifvars H., Harjunkoski I., Pörn R. An extended cutting plane method for a class of non-convex MINLP problems. Computers and Chemical Engineering. 22:1998;357-368.