An enhanced surrogate-assisted differential evolution for constrained optimization problems

The application of evolutionary algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed replace those simulations. In work, a surrogate-assisted procedure is proposed. combines differential evolution method $$k$$ -nearest neighbors (k-NN) similarity-based model. approach, database that stores solutions evaluated exact model, which are used approximate new solutions, managed according merit scheme. Constraints handled rank-based technique builds multiple separate queues based on values function and violation each constraint. Also, avoid premature convergence method, strategy triggers random reinitialization population considered. performance proposed assessed numerical experiments using 24 constrained benchmark 5 mechanical problems. results show achieves optimal remarkably reduction in number compared literature.