A special issue of International Journal of Modelling, Identification and Control
Particle swarm optimisation (PSO) is a new swarm intelligent modelling algorithm proposed by J.Kennedy and R.C.Eberhart since 1995. It simulates animal collective behaviours such as bird flocking and fish schooling. Due to its simple concepts, fast convergent speed, and easy implementation, PSO has been widely applied in many research areas and real-world engineering fields, such as task assignment and scheduling, reactive power and voltage control etc.
The objective of this special issue is to bring researchers from academia and industry together to report and review the latest progresses in this field, and to explore future directions.
Relevant topics include, but are not limited to, the following:
- Theoretical advances in particle swarm optimisation methodology
- Particle swarm optimisation for complex constrained optimization problems
- Particle swarm optimisation for multi-objective optimisation problems
- Particle swarm optimisation for dynamic environment optimisation
- Hybrid methods combined with other optimisation techniques
- Learning and search strategies in particle swarm optimisation
- Applications of particle swarm optimisation, such as bioinformatics and cheminformatics, computational biology, etc.
- Future direction on particle swarm optimisation
Deadline for submission: 1 September, 2008