When Dr Mardé Helbig, senior lecturer in the department of computer science at the University of Pretoria, found that little work had been done on solving problems with conflicting objectives that change over time, known as dynamic multi-objective optimisation problems (DMOOPs), she began to focus on solving DMOOPs using vector-evaluated particle swarm optimisation.

These are algorithms which are simulated or inspired by biological behaviours of animal or birds and have been used to find the optimal solution to a given problem. ”Many real world optimisation problems are dynamic,” says Helbig. ”They have more than one objective, with at least two of those objectives in conflict with one another, and at least one objective or constraint changing over time,” she says. Research in this area can be applied to optimising the treatment of water based on what it’s going to be used for: the scheduling of jobs at a production plant or the routing of vehicles.