Requirements tracing of natural Language artifacts consists of document parsing, Candidate Link Generation, evaluation and analysis. Candidate Link Generation deals with checking if the high-level artifact has been fulfilled by the low-level artifact. Requirements traceability is an important activity undertaken as part of ensuring the quality of software in the early stages of the Software Development Life Cycle (SDLC). The Semantic Relatedness between the terms is not considered in the existing system; hence the Candidate Link Generation is not effective. In the proposed system, a hybrid technique combining both the Semantic Ranking and Pheromone Swarm is implemented. Simple swarm agents are given freedom to operate on their own, determining the search path randomly based on the environment. Pheromone swarm agent decides on what term to select or what path to take is influenced by presence of pheromone markings on the inspected object. Semantic Graph is constructed using semantic relatedness between two terms, computed based on highest value path connecting any pair of the terms. The performance is evaluated with Simple, Pheromone and Semantic Pheromone Swarm techniques. The Semantic Pheromone Swarm provides better results when compared to Simple and Pheromone Swarm Techniques.