Multiple objective optimisation applied to route planning

This paper presents an evaluation of the benefits of multi-objective optimisation algorithms, compared to single objective optimisation algorithms, when applied to the problem of planning a route over an unstructured environment, where a route has a number of objectives defined using real-world data sources. The paper firstly introduces the problem of planning a route over an unstructured environment (one where no pre-determined set of possible routes exists) and identifies the data sources, Digital Terrain Elevation Data (DTED) and NASA Landsat Hyperspectral data, used to calculate the route objectives (time taken, exposure and fuel consumed). A number of different route planning problems are then used to compare the performance of two single-objective optimisation algorithms and a range of multi-objective optimisation algorithms selected from the literature. The experimental results show that the multi-objective optimisation algorithms result in significantly better routes than the single-objective optimisation algorithms and have the advantage of returning a set of routes that represent the trade-off between objectives. The MOEA/D and SMPSO algorithms are shown, in these experiments, to outperform the other multi-objective optimisation algorithms for this type of problem. Future work will focus on how these algorithms can be integrated into a route planning tool and especially on reducing the time taken to produce routes.

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