A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering.

ABSTRACT: Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimization (SSO), and other nature-inspired algorithms have found some success in solving clustering problems but they may converge to local optima due to the lack of balance between exploration and exploitation. In this paper, we propose a novel implementation of SSO, namely social spider

SUBMITTER: Thalamala R 

PROVIDER: S-EPMC7924720 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature