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

## TL;DR

This paper proposes a decentralised multi-demic evolutionary algorithm to solve a dynamic multi-agent travelling salesman problem, enabling on-board, robust, and communication-efficient solutions suitable for real-world distributed agent systems.

## Contribution

It introduces a novel multi-demic EA framework that effectively handles dynamic task allocation and routing in decentralised multi-agent systems, addressing real-world communication constraints.

## Key findings

- Effective decentralised solution for dynamic MATSP
- Robust communication and task exchange among agents
- Improved on-board processing for real-world deployment

## Abstract

The Travelling Salesman and its variations are some of the most well known NP hard optimisation problems. This paper looks to use both centralised and decentralised implementations of Evolutionary Algorithms (EA) to solve a dynamic variant of the Multi-Agent Travelling Salesman Problem (MATSP). The problem is dynamic, requiring an on-line solution, whereby tasks are completed during simulation with new tasks added and completed ones removed. The problem is allocating an active set of tasks to a set of agents whilst simultaneously planning the route for each agent. The allocation and routing are closely coupled parts of the same problem making it difficult to decompose, instead this paper uses multiple populations with well defined interactions to exploit the problem structure. This work attempts to align the real world implementation demands of a decentralised solution, where agents are far apart and have communication limits, to that of the structure of the multi-demic EA solution process, ultimately allowing decentralised parts of the problem to be solved `on board' agents and allow for robust communication and exchange of tasks.