Self-adaptive hybrid differential evolution with simulated annealing algorithm for constrained optimization
SU Qing-hua · Computer Engineering and Applications Journal · 2009
A self-adaptive hybrid differential evolution with simulated annealing algorithm using a constraint-handling approach based on feasibility rules,termed SahDESAfr,is proposed to solve real-parameter constrained optimization problems.In the SahDE-SAfr algorithm,the choice of learning strategy and several critical control parameters are not required to be pre-specified.During evolution,the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience.A simple constraint-handling approach based on feasibility rules is employed to deal with inequation constraints.The performance of the SahDESAfr algorithm is evaluated on a set of well-know constrained optimization problems commonly adopted in the specialized literature.The performance of the SahDESAfr is evaluated on the set of 13 benchmark functions.The proposed approach is compared with respect to two techniques that are representative of the state-of-the-art in the area.Comparative study exposes the SahDESAfr as a competitive algorithm for constrained optimization.