Using Random Local Search helps in avoiding Local Optimum in Differential Evolution

Differential Evolution is a stochastic and meta-heuristic technique that has been proved powerful for solving real-valued optimization problems in high-dimensional spaces. However, Differential Evolution does not guarantee to converge to the global optimum and it is easily to become trapped in a local optimum. In this paper, we aim to enhance Differential Evolution with Random Local Search to increase its ability to avoid local optimum. The proposed new algorithm is called Differential Evolution with Random Local Search (DERLS). The advantage of Random Local Search used in DERLS is that it is simple and fast in computation. The results of experiments have demonstrated that our DERLS algorithm can bring appreciable improvement for the acquired solutions in difficult optimization problems.