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ComplexityIntroductionProblemPerformanceMachineApplicationsSystemEvaluationResultsOptimization algorithmSolveTheoryApproach
Complexity2
  • In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. (hindawi.com)
  • The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 2.16×, 35.42× and 40.37× improvements in energy efficiency for a projected random resistive memory-based hybrid analogue-digital system over a state-of-the-art graphics processing unit and 99.35%, 99.99% and 91.40% reductions of backward pass complexity compared with conventional graph learning. (nature.com)
Introduction1
  • Introduction combined optimization algorithm among the algorithms With the rapid development of mobile robots, path planning [10-12]. (sagepub.com)
Problem2
  • e simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which e﫿ectively solves the global path planning problem of mobile robot. (sagepub.com)
  • Many scholars have improved the traditional ant optimization algorithms to solve the problem of path colony algorithm. (sagepub.com)
Performance1
Machine1
  • This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. (techscience.com)
Applications1
  • Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. (nature.com)
System1
  • At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. (sagepub.com)
Evaluation1
  • As a heuristic algorithm, it simulates the foraging according to certain evaluation criteria, such as the shortest process of the ant colony and obtains the solution path route, the least turning, the least energy consumption, etc. jointly planned by the ants. (sagepub.com)
Results2
  • The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm. (hindawi.com)
  • The results point to a promising direction for next-generation artificial intelligence systems for graph learning. (nature.com)
Optimization algorithm2
  • It combines the optimization algorithm of the finite temperature string (FTS) method and the sampling power of an adaptive biasing potential (ABP) scheme. (nih.gov)
  • An optimization algorithm was designed for selection of the most economical coil currents. (nih.gov)
Solve2
  • 2) The TSP problem is defined, then some algorithms commonly used to solve the TSP problem are introduced, and then the theory and method of the SOM neural network and the basic principle of the ORC_SOM algorithm are introduced in detail. (hindawi.com)
  • By leveraging the powerful laws of quantum mechanics, such as superposition and entanglement, quantum machine learning has the potential to solve these problems significantly faster than classical algorithms. (qulabs.ai)
Theory1
  • 3) Through a large number of experiments, the results prove the validity of the algorithm in this paper and the rationality of the theory. (hindawi.com)
Approach1