A differential evolution with an orthogonal local search

Differential evolution (DE) is a kind of evolutionary algorithms (EAs), which are population based heuristic global optimization methods. EAs, including DE, are usually criticized for their slow convergence comparing to traditional optimization methods. How to speed up the EA convergence while keeping its global search ability is still a challenge in the EA community. In this paper, we propose a differential evolution method with an orthogonal local search (OLSDE), which combines orthogonal design (OD) and EA for global optimization. In each generation of OLSDE, a general DE process is used firstly, and then an OD based local search is utilized to improve the quality of some solutions. The proposed OLSDE is applied to a variety of test instances and compared with a basic DE method and an orthogonal based DE method. The experimental results show that OLSDE is promising for dealing with the given continuous test instances.