Solving dynamic optimisation problems with revolutionary algorithms

Abstract

The optimisation of dynamic problems is both widespread and difficult. When conducting dynamic optimisation, a balance between re-initialisation and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimise a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make optimisations more efficient. But they adapt slowly to changing environments. We thus suggest a confluence of these approaches: revolutionary algorithms. These algorithms seek to extend the evolutionary and cultural aspects of the former two approaches with a notion of the political. By modelling how belief systems are changed by means of revolution, these algorithms provide a framework to model and optimise dynamic problems in an efficient fashion. The superiority of revolutionary algorithms over cultural and purely genetic algorithms is demonstrated in the solving of a standard dynamic facility location problem.