Hybrid differential evolution with a simplified quadratic approximation for constrained optimization problems

Incorporating local search heuristics is often very useful in designing an effective evolutionary algorithm. In this article a simplified quadratic approximation (SQA) is used as a local search operator for enhancing the performance of standard differential evolution (DE). The emphasis of this article is placed on demonstrating how this local search scheme can improve the performance of DE for the constrained optimization. The proposed approach was tested with a well-known constrained benchmark suite and four engineering design problems. Experimental results show that the proposed hybrid DE with SQA performs better than the standard DE. In addition, the results obtained are very competitive when comparing the proposed approach against other state-of-the-art techniques and some DE-based methods. The proposed approach is also compared with a modified version of the evolution strategy and a modified DE strategy for engineering design problems.