A New Local Searching Strategy for Global Optimization with a Large Number of Local Optimum

For global optimization, because there are a lot of local optimal solutions in problems, differential evolution will face a huge challenge and the efficiency and effectiveness for most of them will be much reduced. In this paper, a new local searching method is proposed in designing a novel evolutionary algorithm for global optimization. Therefore, we construct a new algorithm called differential evolution with a new local searching for global optimization. The simulations are made on standard benchmark suite. The results indicate the proposed algorithm is more effective and efficient.