An Archived Differential Evolution Algorithm for Constrained Global Optimization

Many practical engineering problems are naturally global optimization problems with complex constraints and local optimums. However, practicable and effective approaches for those constrained global optimization problems are still insufficient. A modified differential evolution algorithm is put forward for constrained global optimization problems in this work, using a special constraint-handling mechanism based on dynamic penalty functions and fitness calculation of individuals. An archive of solutions is maintained in the evolutionary process so that the best information of previous local optimums can be kept and utilized for the quality estimate of new solutions. Based on the archive of solutions, an iterative control operation is designed in the algorithm to guide the evolutionary process towards a promising space and avoid unnecessary and worthless search processes. Finally, numerical experiments based on a set of eight well-known constrained optimization problems are carried out to investigate the performance of the proposed method, and the experimental results reveal that the proposed algorithm is robust, effective and efficient in solving constrained global optimization problems.