Constrained optimization using two-phase evolutionary programming
- 24 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 262-267
- https://doi.org/10.1109/icec.1996.542372
Abstract
A hybrid of evolutionary programming (EP) and a deterministic optimization procedure applied to a series of nonlinear optimization problems has been proved to be useful when addressing heavily constrained optimization problems in terms of computational efficiency and solution accuracy. The hybrid EP, however, can be applied only if the mathematical form of the objective function to be minimized/maximized and its gradient are known. To remove such restrictions, a two-phase evolutionary programming method is proposed. The first phase uses the standard EP, while the second phase uses the elitist EP with deterministic ranking strategy. Using Lagrange multipliers and gradually putting 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. The comparisons among variants of two-phase EP indicate that the proposed two-phase EP achieves an exact solution with less computation time without reducing convergence stability.Keywords
This publication has 9 references indexed in Scilit:
- On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA'sPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- GENOCOPCommunications of the ACM, 1996
- Hybrid evolutionary programming for heavily constrained problemsBiosystems, 1996
- A Survey of Constraint Handling Techniques in Evolutionary Computation MethodsPublished by MIT Press ,1995
- Preliminary Investigations into a Two-Stage Method of Evolutionary Optimization on Constrained ProblemsPublished by MIT Press ,1995
- Constrained Optimization Via Genetic AlgorithmsSIMULATION, 1994
- Co-evolutionary constraint satisfactionPublished by Springer Nature ,1994
- Genetic Algorithms + Data Structures = Evolution ProgramsPublished by Springer Nature ,1992
- A two-phase optimization neural networkIEEE Transactions on Neural Networks, 1992