Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Using Disruptive Selection to Maintain Diversity in GeneticAlgorithms
Applied Intelligence
Maintaining Genetic Diversity in Genetic Algorithms through Co-evolution
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A comparative study of probability collectives based multi-agent systems and genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary path planner for UAVs in realistic environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Interactive Planning Under Many Objectives: An Application to the Vehicle Routing Problem
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-objective probability collectives
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Exploiting prior information in multi-objective route planning
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Enhancing intill sampling criteria for surrogate-based constrained optimization
Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling
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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.