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Found 16 papers, 0 papers with code
UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment
Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution.
Performance assessment and exhaustive listing of 500+ nature inspired metaheuristic algorithms
To comparatively evaluate the performance of the recent competitive variants and newly proposed metaheuristics, 11 newly proposed metaheuristics and 4 variants of established metaheuristics are comprehensively compared on the CEC2017 benchmark suite.
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Moreover, we also show via an ablation study that our ITS can help achieve a balance between the performance and training efficiency.
Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System
Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices.
Learning by Active Forgetting for Neural Networks
This paper presents a learning model by active forgetting mechanism with artificial neural networks.
An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem
Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants.
Curvature Graph Neural Network
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems
This offers a novel and general paradigm that combines RL with OR approaches to solving scheduling problems, which leverages the respective strengths of RL and OR: The MDP narrows down the search space of the original problem through an RL method, while the mixed-integer programming process is settled by an OR algorithm.
Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.
Integrating Variable Reduction Strategy with Evolutionary Algorithm for Solving Nonlinear Equations Systems
To test the effectiveness of VRS in dealing with NESs, this paper integrates VRS into two existing state-of-the-art EA methods (i. e., MONES and DRJADE), respectively.
Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty
We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit.
Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions
Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs).
Across neighbourhood search for numerical optimization
In this study, a novel population-based across neighbourhood search (ANS) is proposed for numerical optimization.
An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy
In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs).
