23 lines
1.3 KiB
HTML
23 lines
1.3 KiB
HTML
<html>
|
|
<head>
|
|
<title>Particle Swarm Optimization - PSO</title>
|
|
</head>
|
|
<body>
|
|
<h1 align="center">Particle Swarm Optimization - PSO</h1>
|
|
<center>
|
|
</center><br>
|
|
The Particle Swarm Optimization by Kennedy and Eberhardt is inspired by swarm intelligent
|
|
behaviour seen in animals like birds or ants. A swarm of particles is a set of individual agents
|
|
"flying" across the search space with individual velocity vectors. There is no selection as in
|
|
classic Evolutionary Algorithms. Instead, the individuals exchange knowledge about the space they
|
|
have come across. Each one is attracted to the best position the individual has seen so far (cognitive
|
|
component) and to the best position known by its neighbors (social component).<br>
|
|
The neighborhood is defined by the swarm velocity, which may be a linear ordering, a grid and some others.
|
|
The influence of the velocity of the last time-step is taken into account using an inertness/
|
|
constriction parameter, which controls the convergence behaviour of the swarm.
|
|
The influence of social and cognitive attraction are weighed using the <i>phi</i> parameters. In the
|
|
constriction variant there is a dependence enforced between constriction and the phi, making sure that
|
|
the swarm converges slowly but steadily, see the publications of Clerc, e.g. <br>
|
|
|
|
</body>
|
|
</html> |