2.  Introduction  Particle swarm optimization  PSO algorithm  PSO solution update in 2-D  Example

3.  Particle Swarm Optimization(PSO) ◦ Proposed by James Kennedy & Russell Eberhart in 1995 ◦ Inspired by social behavior of birds and fishes ◦ Combines self-experience with social experience ◦ Population-based optimization

4.  Uses a number of particles that constitute a swarm moving around in the search space looking for the best solution.  Each particle in search space adjusts its “flying” according to its own flying experience as well as the flying experience of other particles

5.  Swarm: a set of particles (S)  Particle: a potential solution ◦ Position: ◦ Velocity:  Each particle maintains ◦ Individual best position (PBest)  Swarm maintains its global best (GBest) S Fitness function Fitness value

6.  Basic algorithm of PSO 1.Initialize the swarm form the solution space 2.Evaluate the fitness of each particle 3.Update individual and global bests 4.Update velocity and position of each particle 5.Go to step2, and repeat until termination condition

8.  Original velocity update equation ◦ w,c 1,c 2 : Constant ◦ random 1 (), random 2 (): random variable  Position update

9.  Particle’s velocity GBest Inertia cognitive social PBest x(k) x(k+1) v(k+1) v(k)

10. x(k) - Current solution (4, 2) PBest - Particle’s best solution (9, 1) GBest-Global best solution (5, 10) GBest PBest

11. Current solution (4, 2) Particle’s best solution (9, 1) Global best solution (5, 10) Inertia: v(k)=(-2, 2) x(k) - Current solution (4, 2) PBest - Particle’s best solution (9, 1) GBest-Global best solution (5, 10) GBest PBest

12. Current solution (4, 2) Particle’s best solution (9, 1) Global best solution (5, 10)  Inertia: v(k)=(-2,2)  Cognitive: PBest-x(k)=(9,1)-(4,2)=(5,-1)  Social: GBest-x(k)=(5,10)-(4,2)=(1,8) x(k) - Current solution (4, 2) PBest - Particle’s best solution (9, 1) GBest-Global best solution (5, 10) GBest PBest

13. x(k) - Current solution (4, 2) PBest - Particle’s best solution (9, 1) GBest-Global best solution (5, 10)  Inertia: v(k)=(-2,2)  Cognitive: PBest-x(k)=(9,1)-(4,2)=(5,-1)  Social: GBest-x(k)=(5,10)-(4,2)=(1,8) v(k+1)=(-2,2)+0.8*(5,-1) +0.2*(1,8) = (2.2,2.8) GBest PBest v(k+1)

14. GBest PBest x(k+1) x(k) - Current solution (4, 2) PBest - Particle’s best solution (9, 1) GBest-Global best solution (5, 10)  Inertia: v(k)=(-2,2)  Cognitive: PBest-x(k)=(9,1)-(4,2)=(5,-1)  Social: GBest-x(k)=(5,10)-(4,2)=(1,8)  v(k+1)=(2.2,2.8) x(k+1)=x(k)+v(k+1)= (4,2)+(2.2,2.8)=(6.2,4.8)

15.  Find the minimum of this function

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