This paper presents novel modifications to the Firefly Algorithm (FA) that manipulate the functionality of the intensity and attractiveness of fireflies through the incorporation of grouping behaviours into the movement of the fireflies. FA is one of the most well-known and actively researched swarm-based algorithms, gaining notoriety for the powerful search capability offered and overall computational simplicity. While the FA is an effective optimisation algorithm, it is unfortunately susceptible to the issue of premature convergence and oscillations within the swarm, which can lead to suboptimal performance. In the original FA formulation, at each iteration fireflies will instinctively move towards the most intensely bright firefly which is in closest proximity to them. The algorithm proposed in this paper manipulates the movement of the fireflies through modification of this intensity and attraction relationship, allowing the swarm to move in different ways, ultimately increasing the search diversity within the swarm. While group-based FAs have been proposed previously, the group-based FAs presented in this paper utilise a different approach to creating groups, implementing groupings based upon firefly performance at each iteration, resulting in continually varying groupings of fireflies, to further increase search diversity and maintain computational simplicity.