Journal of Global Optimization
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Collective intelligence and bush fire spotting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary trajectory planner for multiple UAVs in realistic scenarios
IEEE Transactions on Robotics
Evolutionary Route Planner for Unmanned Air Vehicles
IEEE Transactions on Robotics
Optimal scheduling for refueling multiple autonomous aerial vehicles
IEEE Transactions on Robotics
IEEE Transactions on Robotics
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
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This paper introduces and studies the application of Constrained Sampling Evolutionary Algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness the power of Evolutionary Algorithms (EA) when operating in complex, noisy, multimodal optimization problems and transfer the advantages of their approach to real time real world problems that can be transformed into search and optimization challenges. These types of problems are denoted as Constrained Sampling problems and are characterized by the fact that the physical limitations of reality do not allow for an instantaneous determination of the fitness of the points present in the population that must be evolved. A general approach to address these problems is presented and a particular implementation using Differential Evolution as an example of CS-EA is created and evaluated using teams of UAVs in search and rescue missions. The results are compared to those of a Swarm Intelligence based strategy in the same type of problem as this approach has been widely used within the UAV path planning field in different variants by many authors.