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P article S warm O ptimization. Fahimeh Fooladgar. Outline. Swarm Intelligence Introduction to PSO Original PSO algorithms Global Best PSO Local Best PSO Algorithm Aspects Basic Variations PSO Parameters Application. Swarm Intelligence. Example : benefits of cooperation
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Particle Swarm Optimization FahimehFooladgar
Outline • Swarm Intelligence • Introduction to PSO • Original PSO algorithms • Global Best PSO • Local Best PSO • Algorithm Aspects • Basic Variations • PSO Parameters • Application
Swarm Intelligence • Example : benefits of cooperation • Swarm group • agents that communicate with each other • either directly or indirectly • acting on their local environment • Swarm Intelligence (SI) or collective intelligence • emerges from the interaction of such agents • Computational Swarm Intelligence(CSI) • algorithmic models of such behavior
Swarm Intelligence(cont.) • computational models of swarm intelligence • social animals and social insects • ants, termites, bees, spiders, fish schools, and bird flocks • individuals relatively simple in structure • but their collective behavior usually very complex • pattern of interactions between the individuals of the swarm over time
Swarm Intelligence(cont.) • objective of computational swarm intelligence models • simple behaviors of individuals • local interactions with the environment and neighboring • to obtain more complex behaviors • solve complex problems (optimization problems)
Introduction • First introduced by James Kennedy and Russell Eberhart in 1995 • population-based search algorithm • simulation of the social behavior of birds within a flock • Individuals are particles • Individuals follow a very simple behavior • emulate the success of neighboring • emulate their own successes
Introduction (cont.) • swarm of particles : population of individuals • particle have its own velocity • xi (t): position of particle i at t
Introduction (cont.) • velocity vector drives the optimization process • reflects experiential knowledge and socially exchanged information • The experiential knowledge of a particle: cognitive component • distance of the particle from its own best position • particle’s personal best position • socially exchanged information :social component
Original PSO algorithms • Two PSO algorithms • Differ in the size of their neighborhoods • gbest PSO and lbest PSO
Global Best PSO • Neighborhood for each particle is entire swarm • Social network : star topology • Velocity update statement
Global Best PSO (cont.) • vij(t) :velocity of particle i in dimension j = 1, . . . , nx • yij(t) : personal best position • y^j(t) : best position found by the swarm • xij(t) : position of particle i in dimension j • c1 and c2 : positive acceleration constants • scale the contribution of the cognitive and social components • r1j(t), r2j(t) ∼ U(0, 1) • stochastic element to the algorithm
Global Best PSO (cont.) • fitness function • personal best position at the next time step
Global Best PSO (cont.) • global best position or • ns : total number of particles in the swarm
Local Best PSO • smaller neighborhoods are defined for each particle • network topology : ring social • Velocity update statement
Local Best PSO(cont.) • y^ij : best position, found by the neighborhood of particle i in dimension j • best position found in the neighborhood Ni
Local Best PSO(cont.) • neighborhood defined • gbest PSO is a special case of the lbest PSO with nNi = ns
lbest PSO versus gbest PSO • Two main differences • gbest PSO converges faster than lbest PSO less diversity • lbest PSO less susceptible to being trapped in local minima
Velocity Components • vi(t) : previous velocity • memory of the previous flight direction • prevents the particle from drastically changing direction • bias towards the current direction • referred as the inertia component
Velocity Components(cont.) • c1r1(yi −xi ) : cognitive component • drawn back particle to their own best positions, • individuals return to situations that satisfied them most in the past • referred to as the “nostalgia” of the particle
Velocity Components(cont.) • social component • In gbest PSO • In lbest PSO • each particle drawn towards the best position found by the particle’s neighborhood • referred to as the “envy”
Geometric Illustration inertia velocity cognitive velocity social velocity new velocity
Algorithm Aspects • initialize the swarm • Particle position • initial velocities • Initial personal best position
Stopping conditions • Maximum number of iterations • Acceptable solution has been found • No improvement is observed over a number of iterations • if the average change in particle positions is small • if the average particle velocity over a number of iterations is approximately zero
Stopping conditions(cont.) • Objective function slope is approximately zero • If f ’(t) < Є ,the swarm is converged
Social Network Structures Wheel Star Ring Four Clusters Von Neumann Pyramid
Basic Variations • Improve basic PSO • speed of convergence • Quality of solutions • Velocity clamping • Inertia weight • Constriction Coefficient
Velocity Clamping • exploration–exploitation trade-off • Exploration : explore different regions of the search space • Exploitation : concentrate the search around a promising area • good optimization algorithm: balances these contradictory objectives • velocity update equation
Velocity Clamping(cont.) • velocity quickly explodes to large values • Then particles have large position updates • particles diverge • Should control the global exploration of particles • velocities clamped to stay within boundary constraints • Vmax,j denote the maximum allowed velocity in dimension j
Velocity Clamping(cont.) • Large values of Vmax,j facilitate global exploration • smaller values encourage local exploitation
Velocity Clamping(cont.) • If Vmax,j is too small • swarm may not explore sufficiently beyond locally good regions • increase the number of time steps to reach an optimum • swarm may become trapped in a local optimum • If Vmax,j is too large • risk the possibility of missing a good region • particles may jump over good solutions • but particles are moving faster
Velocity Clamping(cont.) • Balance between • moving too fast or too slow • exploration and exploitation • value of δ is problem-dependent
Inertia Weight • introduced by Shi and Eberhart • control the exploration and exploitation abilities of the swarm • eliminate the need for velocity clamping • controlling influence of previous flight direction to new velocity
Inertia Weight(cont.) • value of w is extremely important • ensure convergent behavior • tradeoff exploration and exploitation • For w ≥ 1 • velocities increase over time • the swarm diverges • Particles fail to change direction • For w < 1 • particles decelerate until their velocities reach zero
Inertia Weight(cont.) • guarantees convergent particle trajectories • If this condition is not satisfied, divergent or cyclic behavior may occur
Inertia Weight(cont.) • Dynamic Inertia Weight approaches • Linear decreasing • Start with w(0)=0.9 and final inertia weight w(nt)=0.4 • nt : maximum number of time steps • w(0) is the initial inertia weight • w(nt) is the final inertia weight • w(t) is the inertia at time step t
Inertia Weight(cont.) • Random adjustments • Nonlinear decreasing
Constriction Coefficient • similar to the inertia weight • balance the exploration–exploitation trade-off • velocities are constricted by a constant χ • referred to as the constriction coefficient
Constriction Coefficient(cont.) • Κ controls the exploration and exploitation • For κ ≈ 0 • fast convergence • local exploitation • For κ ≈ 1 • slow convergence • high degree of exploration • Usually, κ set to a constant value • First K set close to one, decreasing it to zero
Constriction Coefficient(cont.) • Constriction approach equivalent to inertia weight approach if
PSO Parameters • Swarm size (ns) • more particles in the swarm, larger the initial diversity of the swarm • general heuristic : ns ∈ [10, 30] • actually problem dependent • Neighborhood size • Smaller neighborhoods , slower in convergence, more reliable convergence to optimal solutions • Best solution : starting with small neighborhoods and increasing the neighborhood • Number of iterations • It depend on problem
PSO Parameters(cont.) • Acceleration coefficients • c1 , c2 , r1 and r2 • control the stochastic influence of the cognitive and social components • c1 : how much confidence a particle in itself • c2 : how much confidence a particle in its neighbors
What makes PSO so attractive to practitioners? • Simplicity • Easy to implement • ns×nx array for particle’s position • ns×nx array particle’s velocity • ns×nx d array particle’s personal best • 1×nx array for global best • 1×nx array for Vmax • Can adapt to different application
What makes PSO so attractive to practitioners? • All operations are simple and easy to implement • It require low computational resources (Memory and CPU) • It has ability to quickly converge to a reasonably good solution • It can easily and effectively run in distributed environments
References • A.P.Engelbrecht, “computational intelligence ”,2007 • http:://www.matlabsite.com • R.Poli, "Analysis of the Publications on the Applications of Particle Swarm Optimisation ", Journal of Artificial Evolution and Applications, Vol. 2008,10 pages, 2007 • K.E. Parsopoulos and M.N. Vrahatis. Particle Swarm Optimizer in Noisy and Continuously Changing Environments. In Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, pages 289–294,2001