Preserving Locality for Optimal Parallelism in Task Allocation
HPCN Europe '97 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
The 4th International Symposium on Experimental Robotics IV
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Solving traveling salesman problems by combining global and local search mechanisms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
IEEE Computational Intelligence Magazine
IEEE Computational Intelligence Magazine
Evolutionary algorithms for real world applications [Application Notes]
IEEE Computational Intelligence Magazine
Sensor-based coverage with extended range detectors
IEEE Transactions on Robotics
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary Gradient Search Revisited
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm for large traveling salesman problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents novel area coverage algorithms that have been validated using Boeing VSTL hardware. Even though the multi-vehicle search area coverage problem is large and complex, several new memetic computing methods have been presented that decompose, allocate and optimize the exploration of a search area for multiple heterogeneous vehicles. These new methods were shown to have good performance and quality, and as they are defined in a general way, these methods are applicable to many other problem domains. The methods have been combined into a mission-planner architecture that is able to adaptively control the behavior of multiple vehicles with dynamic vehicle capabilities and environments for mission assurance. The topic of mission-planning architectures and optimization of swarms of autonomous vehicles is a young and exciting field with many opportunities for research. More computationally efficient methods for decomposition may be useful, as well as the application of next-generation meta-learning architectures for path planning. In addition to the existing collision avoidance, path de-confliction during planning can improve safety and efficiency.