Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks
Jiasen Wang, Jun Wang, Qing‐Long Han
Abstract
This article presents a collaborative neurodynamic optimization (CNO) approach to multivehicle task assignments (TAs). The original combinatorial quadratic optimization problem for TA is reformulated as a quadratic unconstrained binary optimization (QUBO) problem with a quadratic utility function and a penalty function for handling load capacity and cooperation constraints. In the framework of CNO with a population of discrete Hopfield networks (DHNs), a TA algorithm is proposed for solving the formulated QUBO problem. Superior experimental results in four typical multivehicle operation scenarios are reported to substantiate the efficacy of the proposed neurodynamics-based TA approach.
Topics & Concepts
Quadratic assignment problemMathematical optimizationTask (project management)Computer scienceFunction (biology)Optimization problemPopulationDiscrete optimizationPenalty methodQuadratic unconstrained binary optimizationCombinatorial optimizationHopfield networkQuadratic equationArtificial intelligenceArtificial neural networkMathematicsEngineeringSystems engineeringQuantum computerBiologyDemographySociologyQuantumQuantum mechanicsEvolutionary biologyPhysicsGeometryTraffic control and managementMetaheuristic Optimization Algorithms Research