Results 21 to 30 of about 72,837 (301)
, 2023 This chapter provides preliminaries and essential definitions in optimization, meta-heuristics, and swarm intelligence. It starts with different components of optimization problems, formulations, and categories.
Kalayci C.B. +5 more
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Mathematics, 2023 This paper introduces a novel two-step inertial algorithm for locating a common fixed point of a countable family of nonexpansive mappings. We establish strong convergence properties of the proposed method under mild conditions and employ it to solve ...
Rattanakorn Wattanataweekul +2 more
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Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation
Remote Sensing, 2020 This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan ...
Xia Zhao, Wei Chen
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Harnessing machine learning to guide phylogenetic-tree search algorithms
Nature Communications, 2021 Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al.
Dana Azouri +4 more
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New Solutions for Surface Reconstruction from Discrete Point Data by Means of Computational Intelligence [PDF]
, 1998 Surface reconstruction by means of triangulation of digitized point data leads to computational complex optimization problems. Here, deterministic algorithms often result in insufficient solutions or very long computation times.
Albersmann, Frank +3 more
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From recursion to prediction: modeling backtracking effort in TSP with machine learning [PDF]
PeerJ Computer ScienceThe Traveling Salesman Problem (TSP) is a well-known Nondeterministic Polynomial-time (NP)-hard problem in combinatorial optimization. Solving TSP instances optimally using backtracking algorithms guarantees accuracy but incurs significant computational ...
Juan Xie, Zhan Jingchun, Zhu Xunlin
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Dynamic Multi-objective Optimization Using Computational Intelligence Algorithms
, 2023 Multi-objective optimization problems (MOPs) have multiple, often conflicting objectives where an improvement in one objective leads to the worsening of at least one other objective.
Helbig, M
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A Computational Field Framework for Collaborative Task Execution in Volunteer Clouds [PDF]
, 2013 The increasing diffusion of cloud technologies is opening new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing their ...
Lluch-Lafuente, Alberto +7 more
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Evolutionary computation for dynamic optimization problems [PDF]
, 2013 This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering ...
Yao, Xin, Yang, Shengxiang
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Agile Computational Intelligence for Supporting Hospital Logistics During the COVID-19 Crisis
, 2021 24 páginasThis chapter describes a case study regarding the use of `agile¿ computational intelligence for supporting logistics in Barcelona¿s hospitals during the COVID-19 crisis in 2020.
Panadero J. +5 more
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