Research Repository
PhD
Doctorate
Level Doctorate Student Kirsty Montague Status Current Part Time Yes Years 2019 Project Title Evolving Robust Behaviours for Robotic Swarms with a Modular Design Approach Awarding Institution Edinburgh Napier University Director of Studies Emma Hart Second Supervisor Ben Paechter Additional Supervisor Andreas Steyven
MRes
Master's Degree
Level Master's Degree Student Akinwole Obafemi Status Current Part Time Yes Years 2019 Project Title Group activity and travel scheduling with shared autonomous vehicles - A DRL and AnyLogic based solution Awarding Institution Edinburgh Napier University Second Supervisor Ahmed Al-Dubai Additional Supervisor Ben Paechter
PhD
Doctorate
Level Doctorate Student Dr Craig Thomson Status Complete Part Time No Years 2016 - 2021 Project Title Efficient algorithms for MAC layer duty cycling and frame delivery in wireless sensor network Awarding Institution Edinburgh Napier University Director of Studies Ahmed Al-Dubai Second Supervisor Thomas Tan Additional Supervisor Ben Paechter
PhD
Doctorate
Level Doctorate Student Dr. Mandar Gogate Status Complete Part Time No Years 2016 - 2021 Project Title Towards real-time cognitively-inspired multi-modal speech separation: Novel architectures and algorithms Awarding Institution Edinburgh Napier University Director of Studies Amir Hussain Second Supervisor Ahmed Al-Dubai Additional Supervisor Ben Paechter
PhD
Doctorate
Level Doctorate Student Dr Paul Lapok Status Complete Part Time No Years 2015 - 2020 Project Title Evolving planar mechanisms for the conceptual stage of mechanical design Awarding Institution Edinburgh Napier University Director of Studies Alistair Lawson Second Supervisor Ben Paechter
PhD
Doctorate
Level Doctorate Student Dr Andreas Steyven Status Complete Part Time No Years 2013 - 2018 Project Title A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics Project Description Swarm robotics is a special case within the general field of robotics. The distributed nature makes it more resilient with no single point of failure. Considering the application in remote locations, the swarm needs to adapt autonomously to a priori unknown environmental conditions. A special branch of evolutionary robotics achieves online adaptation during runtime through embedding the evolutionary algorithm in the robot. A well studied algorithm to do that is mEDEA, minimal Environment-driven Distributed Evolutionary Adaptation, which has been shown to be able to maintain the swarm's integrity in an abruptly changing environment. It’s completely decentralised nature and the lack of an explicit fitness function leaves only the environment to provide the driving force for the evolutionary process.
This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA.
Firstly, mEDEA is extended with an explicit relative fitness measure while still maintaining the distributed nature of the algorithm. Two ways of using the relative fitness are investigated: influencing the spreading of genomes and performing an explicit genome selection. Both methods lead to an improvement in the swarm’s ability to maintain energy over longer periods.
Secondly, a communication energy model is derived and introduced into the simulator to investigate the influence of accounting for the costs of communication in the distributed evolutionary algorithm where communication is a key component.
Thirdly, a method is introduced that relates environmental conditions to a measure of the swarm’s behaviour in a 3-dimensional map to study the environment’s influence on the emergence of behaviours at the individual and swarm level. Interesting regions for further experimentation are identified in which algorithm specific characteristics
show effect and can be explored.
Finally, a novel individual learning method is developed and used to investigate how the most effective balance between evolutionary and lifetime-adaptation mechanisms is influenced by aspects of the environment a swarm operates in. The results show a clear link between the effectiveness of different adaptation mechanisms and environmental conditions, specifically the rate of change and the availability of learning opportunities.Awarding Institution Edinburgh Napier University Director of Studies Emma Hart Second Supervisor Ben Paechter Thesis A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics
PhD
Doctorate
Level Doctorate Student Dr Kevin Sim Status Complete Part Time Yes Years 2010 - 2014 Project Title Novel hyperheuristics applied to the domain of bin packing Project Description Hyper-heuristics (HH) have been described as methodologies that aim to offer “good enough -soon enough - cheap enough” solutions to real world problems.
Many real world problems can be formulated as combinatorial optimisation problems in which the permutation of the problem elements defines the quality of the solution. Common examples include routing, scheduling, timetabling, packing and constraint satisfaction problems which are often combined in real world situations, making the problems more difficult and the methods employed to solve them less robust and less transferable. Problem specific, bespoke applications often prove too costly and time consuming to implement for many real world business applications with little scope for reuse.
The primary goal of the research being conducted is to investigate novel hyper-heuristic approaches for solving combinatorial optimisation problems with the goal of developing novel classification approaches that map a problems composition to the suitability of simple domain specific heuristic techniques for solving the problem.
One recent grouping of HH approaches is to separate them into two categories described as “Heuristics to Select Heuristics" and “Heuristics to Generate Heuristics"
To date success has been gained while investigating the first category using classification algorithms to select, based on a problem instances characteristics, which from a set of domain specific heuristics will fare best.
Current research is focussing upon generating heuristics using evolutionary programming techniques to incorporate into the system developed to date with the eventual aim of combining automated heuristic design and heuristic selection techniques into a continually adapting problem solver.Awarding Institution Edinburgh Napier University Director of Studies Emma Hart Second Supervisor Ben Paechter Thesis Novel Hyper-heuristics Applied to the Domain of Bin Packing
PhD
Doctorate
Level Doctorate Student Dr Neil Urquhart Status Complete Part Time No Years 1999 - 2003 Project Title Solving a real world routing proglem using evolutionary algorithms and multi-agent systems Awarding Institution Edinburgh Napier University Second Supervisor Ben Paechter
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
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