  The growing complexity of real-world problems has motivated computer
scientists to search for efficient problem-solving methods. Metaheuristics
based on evolutionary computation and swarm intelligence are outstanding
examples of nature-inspired solution techniques. Inspired by the social
spiders, we propose a novel Social Spider Algorithm to solve global
optimization problems. This algorithm is mainly based on the foraging strategy
of social spiders, utilizing the vibrations on the spider web to determine the
positions of preys. Different from the previously proposed swarm intelligence
algorithms, we introduce a new social animal foraging strategy model to solve
optimization problems. In addition, we perform preliminary parameter
sensitivity analysis for our proposed algorithm, developing guidelines for
choosing the parameter values. The Social Spider Algorithm is evaluated by a
series of widely-used benchmark functions, and our proposed algorithm has
superior performance compared with other state-of-the-art metaheuristics.
