Hybrid DE Algorithm for the Solution of Bound Constrained Single-Objective Computationally Expensive Numerical Optimization Problems

The Differential Evolution Algorithm is widely used for the purpose of optimization in many fields. This dissertation proposes a Hybrid Differential Evolution Algorithm and examines its feasibility based on the results of CEC'15 expensive benchmark problem optimization. A local search mechanism was used to develop three versions of Hybrid DE. All versions of the proposed method were used and compared according to the final feedback of their optimization results. Another comparison with five different methods proposed in the related literature was conducted. The final ranking of all the methods implied that Hybrid DE was always among the top best algorithms that were used for the same purpose.