Results 31 to 40 of about 52,409 (312)
IEEE Access, 2018 Grey wolf optimization (GWO) algorithm is a new population-oriented intelligence algorithm, which is originally proposed to solve continuous optimization problems inspired from the social hierarchy and hunting behaviors of grey wolves. It has been proved
Tianhua Jiang, Chao Zhang
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Arthritis Care &Research, Accepted Article.Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed‐methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah +4 more
wiley +1 more source
A Discrete-Time Homing Problem with Two Optimizers
Games, 2023 A stochastic difference game is considered in which a player wants to minimize the time spent by a controlled one-dimensional symmetric random walk {Xn,n=0,1,…} in the continuation region C:={1,2,…}, and the second player seeks to maximize the survival time in C. The process starts at X0=x>0 and the game ends the first time Xn≤0. An exact expression
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Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems
MATEC Web of Conferences, 2017 In this paper, a discrete variant of TLBO (DTLBO) is proposed for solving the traveling salesman problem (TSP). In the proposed method, an effective learner representation scheme is redefined based on the characteristics of TSP problem.
Wu Lehui, Zoua Feng, Chen Debao
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Robust balanced optimization [PDF]
, 2018 An instance of a balanced optimization problem with vector costs consists of a ground set X, a cost-vector for every element of X, and a system of feasible subsets over X.
Spieksma, F.C.R. +17 more
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A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Classes of discrete optimization problems and their decision problems
Journal of Computer and System Sciences, 1974 In the earlier papers by Karp and Held and by Ibaraki, the representation of a discrete optimization problem given in the form of a discrete decision process (ddp) by a finite state model called a sequential decision process (sdp) was considered. An sdp is a finite automaton with a cost function associated with each state transition.
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A self-learning particle swarm optimizer for global optimization problems [PDF]
, 2011 Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems.
Yang, S +8 more
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Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
IEEE AccessComplex optimization problems, especially those encountered in real-life scenarios, pose significant challenges due to their multifaceted nature and the involvement of numerous variables.
Ravneil Nand +2 more
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