Spread-based elite opposite swarm optimizer for large scale optimization

To prevent the traditional particle swarm optimizer (PSO) from inefficient search in complex problem spaces, this paper presents a novel spread-based elite opposite swarm optimizer (SEOSO) for large scale optimization. Inspired by the dandelion seeds in nature, the seeds can randomly spread by wind and grow better for the next generation. To achieve this, the spread learning and elite opposite learning are introduced in SEOSO. In spread learning, the particles are divided into some subswarms and these subswarms can exchange the particles to get more useful information that improves the diversity of the swarm. In elite opposite learning, the opposite position of the particle is used to exclude the worse direction. The experiments are conducted on 35 benchmark functions to evaluate the performance of SEOSO in comparison with several state-of-the-art algorithms. The comparative results show the effectiveness of SEOSO in solving large scale optimization problems.