A Comparative Analysis of Starling Swarm Algorithm for Optimisation in Dynamic Environment

Abstract. Many real-world phenomena can be modelled as dynamic optimisation problems, where the environment of a problem is dynamic and therefore, conventional methods are not capable of dealing with such situations. In this paper, a novel starlings swarm algorithm is proposed for dealing with such dynamic environments. The proposed starlings algorithm consists of a swarm, which has two distinct behaviours to change in their environment that is mutually exclusive, namely: collective response in terms of communication within the seven nearest neighbours and murmuration in terms of patrolling the search space. This way a diverse population is created while the seven nearest neighbours progressively track the dynamic changes. We established that in problems with a moderate shift severity the best variants are those with topology similar to starlings and results confirmed a significant improvement in those cases that used starlings swarm algorithm, which eliminates the use of independent multi-swarm in the search space.

Keywords: Swarm Intelligence, starlings, Optimisation algorithm, bio-inspired, Environment dynamic.