Multiple objective optimisation applied to route planning

Abstract

This paper presents an evaluation of the benefits of multiobjective 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 predetermined 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.

Physical-geographic factors of terrain trafficability of military vehicles according to Western World methodologies

“…They also stressed that the entire process of data quality control should be systematised and that it should include assessment and quality description stored in metadata. Waldock and Corne (2011) have explored the possibility of cross-country vehicle movement prediction on unstructured open source spatial data using multi-criteria optimisation. Horttanainen and Virrantaus (2004) concluded that uncertainty in data accuracy should be noted throughout the modelling process, and the final user, as the decision maker, should be informed of the level of data inaccuracy.…”

“…All models of vehicle terrain trafficability want to reproduce (in different ways) how overall ground elements slow down the vehicle in relation to its maximum predicted speed (Ahlvin and Haley, 1992). The Heuristic Approach (Waldock and Corne, 2011) of trafficability modelling based on the dynamic mathematical models of the Army Material Command Mobility Model (AMC) and the NATO Reference Mobility Model (NRMM) (Hohmann et al, 2013) were limited by their many input parameters and a multitude mathematical formulas, which made it more difficult for the end users. The invention and development of new computer technologies in the late 1980s enabled implementation of mathematical models into GIS.…”

Enhancing infill sampling criteria for surrogate-based constrained optimization

“…In this study this method , however when the aggregation causes the landscape to be severe and complex, treating the problem as a multiobjective one can achieve better solutions. This is a commonly observed phenomenon [9,47] and several authors have even reformulated single objective problems so they can be solved using multiobjective methods [19,24,27].…”

Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation

“…Research works on MOEA/D generally falls into several aspects. For example, combining MOEA/D with other nature inspired meta-heuristics [8][9][10][11] , investigating on the decomposition approaches [12][13][14] , refining the weight vectors for scalar optimization subproblems [15][16][17] , changing the offspring reproduction methods in MOEA/D [18][19][20] , applying MOEA/D on benchmark and real-world problems [21][22][23][24] , and so on. In MOEA/D, the target MOP is decomposed into a set of scalar optimization subproblems by using conventional aggregation approaches.…”