An Improved Differential Evolution Algorithm for Optimization Problems

There are many optimization problems in the intelligent material and adaptive material fields. Differential evolution (DE) is simple and effective and has been successfully applied to solve optimization problems. And it can be applied to intelligent material field. It is easy to understand and realized and has a strong spatial search capability compared to other evolutionary algorithms. In order to avoid the original versions of DE to remain trapped into local minima and accelerate the optimization process, several approaches have been proposed. The mutation of the classical DE is improved in this paper. It effectively guarantees the convergence of the algorithm and avoids the local minima. Testing and comparing results showed the effectiveness of the algorithm.