A social spider algorithm for global optimization

The growing complexity of real-world problems hasmotivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computa-tion and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, wepropose a novel Social Spider Algorithm (SSA) to solve globaloptimization problems. The framework is mainly based on theforaging strategy of social spiders, which utilize the vibrationsspread over the spider web to determine the position of preys.When tested against benchmark functions, SSA has superiorperformance compared with other metaheuristics, including evo-lutionary algorithms and swarm intelligence algorithms.