A Penalty Function Based Differential Evolution Algorithm for Constrained Optimization
Abstract
Differential evolution (DE) and its various dialects are basically designed for solving unconstrained optimization problems and have been widely used .Adaptive differential evolution with optional external archive (JADE)is one of the efficient and updated versions of DE. This paper enhances the capability of JADE to solve constrained optimization problems (COPs). The enhancement is based on introducing a static penalty function in the selection scheme of JADE to handle constraints. The performance of the modified algorithm, abbreviated as CJADE-S is tested on a well-known test suit of COPs, CEC2006. The experimental results show the better performance of CJADE-S on most of the test problems of CEC2006.
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