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Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
Hyun Myung* and Jong-Hwan Kim**
*Virtual Reality R&D Center, ETRI 161 Kajong-dong Yusong-gu, Taejon 305-350, Korea
**Department of EE, KAIST 373-1 Kusong-dong, Yusong-gu, Taejon 305-701, Korea
Received:March 15, 1999Accepted:July 20, 1999Published:March 20, 2000
Abstract
One of the well-known problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, two-phase evolutionary programming, and Evolian algorithms are not safe from the same problem in the first phase. To overcome this problem, we apply the sharing function to the Evolian algorithm and propose to use the multiple Lagrange multiplier method for the subsequent phases of Evolian. The method develops Lagrange multipliers in each subpopulation region independently and seeks for multiple global optima, if any, in parallel. The simulation results demonstrate the usefulness of the proposed multiple Lagrange multiplier method.
Cite this article as:H. Myung and J. Kim, “Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.2, pp. 158-163, 2000.Data files:
