In this article an extension for the Differential Evolution algorithm is proposed for handling nonlinear constraint functions. From the user point of view, the proposed method allows solving multi-constrained. global optimization problems virtually as easily as unconstrained problems. User is not assumed to provide a feasible solution as a starting point for searching, as required by many other methods. Furthermore, the user is not required to set any penalty parameters, any weights for individual constraints, or any other additional search parameters, as in cases for most penalty function methods. In comparison with the original Differential Evolution algorithm, only the selection operation was modified with a new selection criteria for handling the constraint functions. The proposed method is demonstrated by solving a suite of seven well-known and difficult test problems.
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