Particle swarm optimizer with two differential mutation

In this article, a particle swarm optimization algorithm with two differential mutation (PSOTD) is proposed. In PSOTD, a novel structure with two swarms and two layers (bottom layer and top layer) is designed. The top layer consists of all the personal best particles, and the bottom layer consists of all the particles. We divide the particles in the top layer into two sub-swarms. Two different differential mutation operations with two different control parameters are employed in order to breed the particles in the top layer. Thus, one sub-swarm has a good exploration capability, and the other sub-swarm has a good exploitation capability. Obviously, since the top layer leads the bottom layer, the bottom particles achieve a good trade-off between exploration and exploitation. Under the searching structure, PSO enhances the global search capability and search efficiency. In order to test the performance of PSOTD, 44 benchmark functions widely adopted in the literature are used. The experimental results demonstrate that the proposed PSOTD outperforms most of the other tested variants of the PSO in terms of both solution quality and efficiency. (C) 2017 Published by Elsevier B.V.