Evolutionary programming techniques for constrained optimization problems

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Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP), removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems. © 1997 IEEE.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
1997
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, v.1, no.2, pp.129 - 140

ISSN
1089-778X
URI
http://hdl.handle.net/10203/70697
Appears in Collection
EE-Journal Papers(저널논문)
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