With Moore’s law coming to an end, and, with it, the digital age, alternative novel computing systems have to be explored. Physical systems and processes, just like biological ones, can inspire the design of computer algorithms. We actively research a range of physical phenomena and have implemented a range of algorithms which draw inspiration from aspects of these phenomena. This section provides an overview of some of our work in the area of bio-inspired as well as physics-inspired computing.

Bio-Inspired Computing

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioural patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimisation problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. 

Physics-Inspired Computing

Computationally challenging optimisation problems have always been of special interest by various researchers aroundthe globe. This is primarily due to them often having a very high dimensional search space, or having highly complexand non-linear objective functions at their core, which classical gradient-based methods fail to tackle efficiently. Thishas been the main reason for the development of metaheuristic algorithms that take inspiration from our surroundings(nature, swarms and physical processes) and can provide a computationally cheap yet robust optimisation procedurefor such hard problems at hand. Parallely, researchers have also noticed the undeniable success of modeling physics’processes to study highly complex phenomena, both in real-world and computer science. For instance, resourceallocation problem has been well tackled by statistical mechanics models, while certain aspects of statisticalthermodynamics have been employed to explain micro-evolution of species, and so on. As a result, in spite ofswarm-inspired algorithms being in the forefront as robust optimisers, researchers have shown keen interest inadapting principles and theories of physics and applying them to solve real-world optimisation problems.Recently, the world of metaheuristics has seen the advent of several novel search mechanisms based on variousnon-linear physics processes. The novelty of these approaches lies in the fact that the non-linear physical phenomenaare leveraged as backbones to be modeled upon in order to formulate efficient search algorithms, whose mechanism isquite different from the conventional swarm and evolutionary algorithms. Such physics-inspired algorithms have showngreat promise and robustness as global optimisation strategies.