Hong Kong Institute of Vocational Education (Ts...

CS 4486

Classical PSO Algorithm
Chapter 7
• Another best value that is tracked by the PSO is the best value
obtained so far by any particle in the neighborhood of that particle.
This value is called gbest
• The basic concept of PSO lies in accelerating each particle toward
its pbest and the gbest locations, with a random weighted
acceleration at each time step
1 2 () ( ) () ( ) (1)
(2)
id id id id d id
id id id
V w V c Rand p x c Rand g x
x x V
= ´ + ´ ´ - + ´ ´ -
= +
p Vid : Velocity of each particle in each dimension
Ø i: Particle
Ø d: Dimension
p w : Inertia Weight
p c1、 c2 : Constants
p Rand() : Random
p pid : Best position of each particle
p gd : Best position of swarm
p xid : Current position of each particle in each dimension
Chapter 7
Chapter 7
• The inertia weight w is often chosen between [0.5, 1.5].
• A large inertia weight (w) facilitates a global search while a small inertia weight
facilitates a local search.
• By linearly decreasing the inertia weight from a relatively large value to a small value
through the course of the PSO run gives better PSO performance as compared with
fixed inertia weight settings.
Larger w ----------- greater global search ability
Smaller w ------------ greater local search ability.
• The interval of c1 and c2 is [0, 4.0]. If c2 is higher than c1, the social information would
influence the particle more. If c1 is higher than c2, the particle would be affected more
by its personal best rather than the global best.