Firefly algorithm guided by general centre particle and its application in node localisation of wireless sensor networks

摘要

Firefly algorithm (FA) is a kind of swarm intelligence algorithm that was developed by simulating the behaviour of the flashing of fireflies. However, the population uses only the advantage of the better particles' information to complete optimisation without using the comprehensive information of the population effectively. So, this paper proposes an improved FA, namely firefly algorithm guided by general centre particle (GCPFA), in which the General Centre Particle (GCP) was generated by sharing each particle's historically optimal position information, and each particle would learn from GCP after they learned from the other particles with better performances. The simulation results on 12 benchmark test functions also revealed GCPFA's superiority to the other six famous FAs. In order to improve the unreasonable distribution of sensor nodes randomly and improve the network coverage rate, the above algorithm is applied to optimise the coverage of wireless sensor networks and achieve better optimisation effect.