Research focus: Optimisation in Natural Systems: Ants and Slime Moulds
Our modern societies are constantly faced with dynamic optimisation problems. How to route a phone call through a busy network? In what order should one build an aeroplane so that both time and costs are kept to a minimum? Such and similar problems are increasingly being solved using nature-inspired optimisation algorithms. For example the routing of phone calls can be achieved using an algorithm that mimics the way an ant colony finds the shortest path to a food source. More recent is the development of self-organised artificial systems that contain many autonomous components. Such systems are supposed to be able to perform in ways similar to natural systems such as insect colonies. However, the behaviour of artificial systems is only superficially based on real natural systems. This is a shame as natural selection has shaped biological systems to optimally adapt to changing conditions. We should therefore look in more detail at how biological systems solve dynamic optimisation problems.
Social insects collect food in a changing environment without centralised control, and a simple slime mould, which lacks even a central nervous system, can construct an effective and robust food transportation network. I will study both ants and slime moulds in an effort to understand how they solve complex optimisation problems. The problems that I will offer my study organisms will originate from computer science. By comparing two very different study organisms, I will be able to determine if they use similar mechanisms to solve problems or if they utilise fundamentally different methods to solve an identical problem.