A Dimension-Based Elite Learning Particle Swarm Optimizer for Large-Scale Optimization

The large-scale optimization problems (LSOPs) have been a hot research in evolutionary computation (EC) community. Although there have been many contributions from researchers in solving LSOPs, the large search space and the numerous local optimal solutions of LSOPs are still two important challenges. In order to alleviate the above challenges, this paper proposes a dimension-based elite learning particle swarm optimizer (DELPSO). In DELPSO, individuals in the population have their unique update probabilities and select specific learning exemplars according to their own properties, making the evolution of the population more efficient. Meanwhile, in the evolutionary process, each individual chooses two different learning exemplars for each dimension, so that each individual can learn from multiple learning exemplars and using the information from multiple individuals to help its own evolution and enhance the diversity. To testify the effectiveness of the proposed algorithm, DELPSO and some large-scale algorithms are experimented on a widely used large-scale benchmark suite IEEE 2013 and the experimental results show that DELPSO outperforms other comparative algorithms in general.