A parallel social spider algorithm based on population mining

Abstract

The social spider algorithm is a meta-heuristic algorithm inspired by the foraging behavior of biological social spiders, and is initially used to solve optimization problems with continuous functions. There exist many challenges when dealing with discrete combinatorial optimization problems. Therefore, a novel parallel social spider algorithm based on population mining is proposed. Its starting point is to mine dynamic heuristic information from the population of social spiders that cause vibrations to guide the spider’s next walk. It proposes the concept of vibration candidate sets and enhances the behavior of artificial spiders. Furthermore, it is implemented in parallel on the graphics processing unit. According to our investigation, it is the first parallel social spider algorithm. The classical traveling salesman problem is solved as an example. By solving 30 small-scale and medium-scale problems and comparing with other algorithms, it shows that its solution quality is significantly better. By solving 17 large-scale problems, compared with the parallel ant colony system and parallel iterative hill climbing algorithm on the GPU, it is found that both the solution quality and performance are also significantly better. Codes are available at https://github.com/BuptCIAGroup/P-SSA.