Farmani_Wright_IEEE_Trans_Evol_Comp.pdf (452.45 kB)
Self-adaptive fitness formulation for constrained optimization
journal contribution
posted on 2008-10-08, 14:16 authored by Raziyeh Farmani, Jonathan WrightJonathan WrightA self-adaptive fitness formulation is presented for
solving constrained optimization problems. In this method, the dimensionality
of the problem is reduced by representing the constraint
violations by a single infeasibility measure. The infeasibility
measure is used to form a two-stage penalty that is applied to the
infeasible solutions. The performance of the method has been examined
by its application to a set of eleven test cases from the specialized
literature. The results have been compared with previously
published results from the literature. It is shown that the method
is able to find the optimum solutions. The proposed method requires
no parameter tuning and can be used as a fitness evaluator
with any evolutionary algorithm. The approach is also robust in
its handling of both linear and nonlinear equality and inequality
constraint functions. Furthermore, the method does not require an
initial feasible solution.
History
School
- Architecture, Building and Civil Engineering
Citation
FARMANI, R. and WRIGHT, J., 2003. Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 7 (5), pp. 445- 455Publisher
© IEEEPublication date
2003Notes
This is a journal article. It was published in the journal IEEE Transactions on Evolutionary Computation [© IEEE) and is also available at: http://ieeexplore.ieee.org. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.ISSN
1089-778XLanguage
- en