(untitled) (JOURNAL OF SOFTWARE ENGINEERING TOOLS & TECHNOLOGY TRENDS, 2024)
The rising complexity of real-world problems has prompted researchers and computer scientists to look for more effective ways to solve difficulties. Generally inspired by nature, bioinspired metaheuristics based on swarm intelligence algorithms and evolutionary computation have been widely employed to solve complicated, real-world optimization problems because of their adaptability to a broad variety of factors. The social spider optimization (SSO) approach is introduced in this article as a swarm-based algorithm, drawing inspiration from the cooperative behaviors exhibited by social spiders. Search agents in SSO describe a group of spiders that collectively move in accordance with a biological behavior in the colony. Many changes have been made to the SSO algorithm in the years since it was first introduced, improving its performance and enabling its use in other sectors. The developments and uses of the SSO are reviewed in this study.