Optimisation Of Boids Swarm Model Based On Genetic Algorithm And Particle Swarm Optimisation Algorithm (Comparative Study)
Saleh Alaliyat, Harald Yndestad, Filippo Sanfilippo · 2014
In this paper, we present two optimisation methods for a generic boids swarm model which is derived from the original Reynolds’ boids model to simulate the aggregate moving of a fish school. The aggregate motion is the result of the interaction of the relatively simple behaviours of the individual simulated boids. The aggregate moving vector is a linear combination of every simple behaviour rule vector. The moving vector coefficients should be identified and optimised to have a realistic flocking moving behaviour. We proposed two methods to optimise these coefficients, by using genetic algorithm (GA) and particle swarm optimisation algorithm (PSO). Both GA and PSO are population based heuristic search techniques which can be used to solve the optimisation problems. The experimental results show that optimisation of boids model by using PSO is faster and gives better convergence than using GA.