Curves and surfaces for computer aided geometric design: a practical guide
Curves and surfaces for computer aided geometric design: a practical guide
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Evolutionary path planner for UAVs in realistic environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Application of Improved Particle Swarm Optimization Algorithm in UCAV Path Planning
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Multiple UAV path planning using anytime algorithms
ACC'09 Proceedings of the 2009 conference on American Control Conference
Information Sciences: an International Journal
Evolutionary trajectory planner for multiple UAVs in realistic scenarios
IEEE Transactions on Robotics
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
Genetic algorithm based approach for Multi-UAV cooperative reconnaissance mission planning problem
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
A MPC and genetic algorithm based approach for multiple UAVs cooperative search
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - 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
Evolutionary Route Planner for Unmanned Air Vehicles
IEEE Transactions on Robotics
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Evolutionary algorithm based offline/online path planner for UAV navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Journal of Intelligent and Robotic Systems
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The big number of evolutionary planners for Unmanned Aerial Vehicles (UAVs) that have been developed demonstrates the good acceptance that the evolutionary techniques enjoy within the UAV community. However, the minor or nonexistent statistical characterization of the results obtained by the majority of the planners not only makes it difficult to assess their actual performance but also to justify the selection and/or parameterization of their supporting algorithms. To fill the gap, this paper proposes a method for comparing the planners performance by jointly employing several general and problem-specific quality indexes, which take into account the complexity and particularities of the problem. The generality of the performance metrics adopted, which are able to deal with any multi-objective dominance definition, makes them equally applicable to multi-objective planners with different relation operations (such as Pareto dominance, weighted objectives aggregation, and others). The specificity of the other indexes, which consider the types of solutions preferred by the problem experts, makes them especially attractive to characterize their planners' behavior. The paper also shows how to analyze the results of the quality indexes graphically in order to identify, for a particular UAV planning problem, the best planners within a set of 36 variants (based on Genetic Algorithms, Particle Swarm Optimization and Differential Evolution).