Particle Swarm Optimization
School of Computer Science and IT, RMIT University
Date and time: 11.30am - 12.30pm, Friday 29 September, 2006
Venue: 10.08.03 (Building 10, Level
8, Room 3)
Abstract:
Particle Swarm Optimization (PSO) is a
population-based stochastic optimization technique mimicking the social
behaviours of animals and insects, such as bird flocking, animal herding, or
fish schooling. PSO was first developed by
James Kennedy and Russell Eberhart in 1995. In recent
years it has gained increasing popularity in the Evolutionary Computation
research community, largely due to the fact that PSO
has been shown to be an effective optimization method for solving difficult
optimization problems. PSO belongs to the
family of Swarm Intelligence techniques, which typically involve studies of
collective behaviour in decentralized systems. Such systems are made up by a
population of simple agents interacting locally with one other and with their
environment. Although there is typically no centralized control dictating the
behaviour of the agents, local interactions among the agents often cause a
global pattern to emerge. Swarm-like algorithms, such as (PSO)
and Ant Colony Optimization (ACO), have
already been applied successfully to solve real-world optimization problems.
PSO share some common characteristics
with Evolutionary Algorithms. Like EAs, PSO
starts with an initial population of randomly generated individuals (i.e., potential
solutions). These individuals are then modified over many
iterations, via ways of simulating the social behaviour of insects or
animals, in an effort to find the optima in the problem space. Unlike EAs, PSO does not
explicitly use evolutionary operators such as crossover and mutation. A
potential solution simply 'flies' through the search space by modifying itself
according to its past experience and its relationship with other individuals in
the population and the environment.
This talk will aim to provide an introduction to Particle Swarm Optimization
(PSO), and if time allows, highlighting some
of the most important computational techniques employed and their recent
development in this rapid growing area of research. Following topics may be covered:
§
Introduction to PSO
§
PSO using
global and local communication topologies
§
Speciation and niching methods in PSO
§
PSO for multiobjective optimization
§
PSO for
optimization in dynamic environments
§
PSO
real-world applications
About the speaker:
Dr Xiaodong Li is a Senior Lecturer in the School
of Computer Science and IT, RMIT
University, Melbourne,
Australia. His research interest includes Artificial
Intelligence, Evolutionary Computation, Artificial Neutral Networks, Swarm
Intelligence, Multiobjective Optimization,
Optimization in Dynamic Environments, and their applications to real-world
problems. Dr. Li is an IEEE member, and a member of SIGEVO (The ACM Special
Interest Group on Genetic and Evolutionary Computation). He serves as a technical committee member of Working Group on Swarm
Intelligence, IEEE Computational Intelligence Society. He was the
organizing chair for special session on Swarm Intelligence in CEC'03
and CEC'04, and again in CEC'06.
He is the tutorial and special sessions chair for SEAL'06, and the publicity
chair and a member of the steering committee for the IEEE Swarm Intelligence
Symposium 2007 (SIS'07).
Seminar Organisation
Seminars are free and open to the general public. No booking is necessary.
If you are interested in giving a presentation in this seminar series, or to
make suggestions for speakers, please contact Xiaodong Li, the seminar
co-ordinator.