Evolian: Evolutionary Optimization Based on Lagrangian with Constraint Scaling

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In this paper, an evolutionary optimization method, Evolian, is proposed for the general constrained optimization problem, which incorporates the concept of (1) a multi-phase optimization process and (2) constraint scaling techniques to resolve problem of ill-conditioning. In each phase of Evolian, the typical evolutionary programming (EP) is performed using an augmented Lagrangian objective function with a penalty parameter fixed. If there is no improvement in the best objective function in one phase, another phase of Evolian is performed after scaling the constraints and then updating the Lagrange multipliers and penalty parameter. This procedure is repeated until a satisfactory solution is obtained. Computer simulation results indicate that Evolian gives outperforming or at least reasonable results for multivariable heavily constrained function optimization as compared to other evolutionary computation-based methods.
Publisher
Springer
Issue Date
1997-04
Language
English
Citation

Lecture Notes on Computer Science (LNCS) 1213, v.1213, pp.177 - 188

ISSN
0302-9743
DOI
10.1007/BFb0014810
URI
http://hdl.handle.net/10203/12730
Appears in Collection
EE-Journal Papers(저널논문)
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