Bart van Greevenbroek

 Authors  The Paper  Particle Swarm Optimization  Algorithm used with PSO  Experiment  Assessment  conclusion

Ying-ping Chen Ying-yin Lin

 Published in 2009

 Developed by Kennedy and Eberhart  Published in 1995  Inspired by flocking of birds and schools of fish  Solution is modeled as a flying particle in a hyper-plane

= velocity of particle i at the next timestep = the weight for the previous velocity = the best position where this particle had been = the overall global best position ever achieved by the swarm = cognitive and social parameters, deciding the influence of P bls and P bgs = random factor, to produce varied paths. = position of particle i at the current timestep.

 Every particle has an objective function, which can influence a and.  It does not take obstacles into account, making PSO incompatible for crowd simulation in its current form.

 Each pedestrian is considered a particle in 2d space, with position p i = [p ix, p iz ] T a direction D i = [D ix,D iz ] T and a speed S.

and are unit vectors.

The new position is determined by the direction and the speed.

 Speed is updated to the inverse of the objective function. This varies the pace of each person.  If a particle approaches an obstacle, the speed will be slower due to greater objective values.

= balancing factor that decides the balance between avoiding obstacles and reaching the goal. = low if the cost to the goal is high. = the object that has the highest cost (closest obstacle)

is a constant factor that can influence the probability of the new position being accepted.

 A number of experiments were performed  To show how bad this method is.

 No Details on the implementation are given  No system specs  No performance  No way to compare with other methods  Except the movies which show very non- human like behavior

 Swarm Intelligence is NOT a good way to model human behavior  Other predictive methods look much nicer.  The desire to make something general will not work when you have specific situations requiring specific solutions.

 In the abstract the authors state that they want to avoid oscillations which works with the original PSO. But the examples shown oscillate like ants