Details of Grant 

EPSRC Reference:	EP/G002339/1
Title:	Cooperatively Coevolving Particle Swarms for Large Scale Optimisation
Principal Investigator:	Yao, Professor X
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	School of Computer Science
Organisation:	University of Birmingham
Scheme:	Standard Research
Starts:	21 July 2008	Ends:	20 October 2009	Value (£):	28,341
EPSRC Research Topic Classifications:	Artificial Intelligence New & Emerging Comp. Paradigms
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
In this project, we will develop novel cooperative coevolutionary particle swarm algorithms for solving large scale optimisation problems, especially problems characterised by high dimensionality and non-separability. Although there have been some work on cooperative coevolutionary algorithms (CCEAs) for optimisation, these CCEAs break down quickly when dealing with non-separable high dimensional problems (e.g., with 100 or more real-valued variables). For this class of problems, more effective problem decomposition strategies are urgently needed. We will develop adaptive decomposition strategies capable of decomposing a large problem into subcomponents where the interdependencies among different subcomponents are kept at minimum. These more effective decomposition strategies will then be incorporated into a Particle Swarm Optimisation (PSO) algorithm to enhance PSO's ability in handling highdimensional non-separable problems, an area that PSO is currently very weak in.Classical PSO algorithms have been shown to perform well on low dimensionalproblems, but poorly on high dimensional problems. By combining a cooperativecoevolutionary framework with a PSO model, more effective PSO algorithms forlarge scale problems are expected to be developed. We will carry out in-depth theoretical analysis and computational studies of different adaptive decomposition strategies and cooperative coevolutionary PSO algorithms (CCPSO). Comprehensive comparisons between proposed CCPSO algorithms and other existing CCEAs will be carried out using both separable and non-separable benchmark functions with dimensions up to 1000 (real-valued variables). This will allow us to identify the strengths and weakness of our proposed algorithms in handling this particular class of problems. To evaluate further the performance of proposed CCPSO algorithms, a real-world application in shape optimisation will be used. The expected outcomes of this research will benefit not only researchers in the evolutionary computation and swarm intelligencecommunities, but also practitioners in real-world optimisation.
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Organisation Website:	http://www.bham.ac.uk