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cs.NE updates on arXiv.org

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? 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Uav Route Planning For Maximum Target Coverage
Murat Karakaya · 2014-03-12 · via cs.NE updates on arXiv.org

Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of UAVs to cover maximum number of targets considering their flight range. The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach, based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success of the proposed MMAS method by increasing the number of covered targets compared to the solution based on the NN heuristic.

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