Abstract :
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Particle swarm optimization (PSO) is a population
based stochastic optimization technique developed
by Dr. Eberhart and Dr. Kennedy in 1995, inspired
by social behavior of bird flocking or fish
schooling.
PSO shares many similarities with evolutionary
computation techniques such as Genetic Algorithms
(GA). The system is initialized with a population of
random solutions and searches for optima by
updating generations. However, unlike GA, PSO
has no evolution operators such as crossover and
mutation. In PSO, the potential solutions, called
particles, fly through the problem space by
following the current optimum particles.
Compared to GA, the advantages of PSO are that
PSO is easy to implement and there are few
parameters to adjust. PSO has been successfully
applied in many areas: function optimization,
artificial neural network training, fuzzy system
control, and other areas where GA can be applied.
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