Towards a novel optimisation approach with simultaneous knowledge acquisition for distributed computing environments

Current optimisation methods are of limited use for decision-support in complex systems due to two main short-comings. Firstly, they require long computational times to identify optimal solutions for large-scale problems and the algorithms are not easily parallelised for use in large-scale distributed computing environments. Such environments become increasingly available with the advent of Grid Technologies and new generations of optimisation methods are required that can exploit the vast available distributed computing resources effectively. Secondly, the results obtained from optimisation runs are often difficult to interpret by the user in the context of the decisions to be taken. This is particularly true for stochastic optimisation methods, where important solution features are often blurred by features not strongly impacting on the systems performance. An optimisation approach that could provide the user with optimal solutions alongside insights into the importance of individual solution features would be very attractive for decision-support.