Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental results based on a large number of instances (with up to 10,000 vertices) show that, this new approach clearly outperforms the existing machine learning based TSP algorithms, and significantly improves the generalization ability of the trained model

Solving real-world routing problems using evolutionary algorithms and multi-agent-systems

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces. The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representation designed specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of problem instances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system produces routes that are on average slightly longer than those produced by the TSP solver. Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented that makes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctions or by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructing delivery networks meeting set constraints, over a range of problem instances and constraint values.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Solving Real-World Routing Problems using Evolutionary Algorithms and Multi-Agent-Systems.

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces.The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representationdesigned specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of probleminstances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system producesroutes that are on average slightly longer than those produced by the TSP solver.Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented thatmakes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctionsor by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructingdelivery networks meeting set constraints, over a range of problem instances and constraint values