BIO-INSPIRED PARTICLE SWARM OPTIMIZATION



  PSO PRATYUSHA-017.ppt (Size: 792 KB / Downloads: 1)

INTRODUCTION


It was developed in 1995 by James Kennedy (social-psychologist) and Russell Eberhart (electrical engineer).
PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms.
Belongs to the categories of Swarm Intelligence techniques and Evolutionary Algorithms for optimization.
The motivation of PSO algorithm was social behavior such as bird flocking, and fish schooling.
PSO is a population-based method, like Genetic algorithm. However, the basic concept is cooperation instead of rivalry.
The term swarm (shoaling, swarming or flocking) is applied to fish, insects, birds and microorganisms, such as bacteria, and describes a behavior of an aggregation of animals of similar size and body orientation, generally cruising in the same direction.


PSO features


PSO is very similar to GA, but it does not have genetic operators (crossover and mutation).
A particle moves with the velocity:
its own experience,
experience from all particles.
The idea is similar to bird flocks searching for food.
Optimization problem representation is similar to the genes encoding methods used in GAs but for PSO the variables are called dimensions, that create a multi-dimensional hyperspace.
"Particles" fly in this hyperspace and try to find the global minima/maxima, their movement being governed by a simple mathematical equation.


PROPERTIES OF PARTICLES


Ability to exchange information with its neighbors.
Ability to memorize a previous position
Ability to use information to make a decision.
There is no selection in PSO: e.g. all particles survive for the length of the run, secondly PSO is the only EA that does not remove candidate population members.
In PSO, topology is constant; a neighbor is a neighbor.


PSO APPLICATION


Economic dispatch considering the generator constraints
A hybrid particle swarm optimization applied to loss power minimization
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization
Congestion Management Using Multiobjective Particle Swarm Optimization


Advantages

Simple in concept
Easy to implement
Computationally efficient
Binary PSO