FlockOpt: A new swarm optimization algorithm based on collective behavior of starling birds

摘要

The aim of this paper is to introduce a new algorithm, FlockOpt, for real-parameter optimization. The proposed algorithm is inspired by a recent model of the flocking behaviour of starling birds and combines the main elements of this model with additional features from Swarm Intelligence. The results of from FlockOpt are compared to the results of generic versions of Particle Swarm Optimization, which is the closest relative of FlockOpt from the Swarm Intelligence field. The comparison shows that FlockOpt is able to beat the generic versions of particle swarm optimization in the majority of the test cases. Interesting features such as the attraction, repulsion and the alignment between members of the population make FlockOpt quite attractive for further examination.