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The Travelling Salesman and its variations are some of the most well known NPhard optimisation problems. This paper looks to use both centralised anddecentralised implementations of Evolutionary Algorithms (EA) to solve adynamic variant of the Multi-Agent Travelling Salesman Problem (MATSP). Theproblem is dynamic, requiring an on-line solution, whereby tasks are completedduring simulation with new tasks added and completed ones removed. The problemis allocating an active set of tasks to a set of agents whilst simultaneouslyplanning the route for each agent. The allocation and routing are closelycoupled parts of the same problem making it difficult to decompose, insteadthis paper uses multiple populations with well defined interactions to exploitthe problem structure. This work attempts to align the real worldimplementation demands of a decentralised solution, where agents are far apartand have communication limits, to that of the structure of the multi-demic EAsolution process, ultimately allowing decentralised parts of the problem to besolved `on board' agents and allow for robust communication and exchange oftasks.

Decentralised Multi-Demic Evolutionary Approach to the Dynamic Multi-Agent Travelling Salesman Problem