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Research on microseismic localization algorithm with global search and local optimization
Heliyon, 2023 A microseismic localization algorithm that combines global search and local optimization is proposed. The Fewer Conditions Trigger Difference (FCTD) objective function of global search and local optimization is constructed, the execution process of the ...
Yimin Song +4 more
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Hybridization of Decomposition and Local Search for Multiobjective Optimization [PDF]
IEEE Transactions on Cybernetics, 2014 Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD).
Roberto Battiti +2 more
exaly +6 more sources
Particle Swarm Optimization with Enhanced Global Search and Local Search
Journal of Intelligent Systems, 2017 In order to mitigate the problems of premature convergence and low search accuracy that exist in traditional particle swarm optimization (PSO), this paper presents PSO with enhanced global search and local search (EGLPSO).
Wang Jie, Li Hongwen
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Bayesian Optimization with Local Search [PDF]
, 2020 Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple starting points. In this work we propose a new multi-start algorithm where the starting points are determined in a Bayesian ...
Yuzhou Gao, Tengchao Yu, Jinglai Li
openaire +4 more sources
Multi-store collaborative delivery optimization based on Top-K order-split.
PLoS ONE, 2023 Regarding the fulfillment optimization of online retail orders, many researchers focus more on warehouse optimization and distribution center optimization.
Yanju Zhang, Liping Ou, Jiaxu Liu
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Local policy search with Bayesian optimization
CoRR, 2021 Reinforcement learning (RL) aims to find an optimal policy by interaction with an environment. Consequently, learning complex behavior requires a vast number of samples, which can be prohibitive in practice. Nevertheless, instead of systematically reasoning and actively choosing informative samples, policy gradients for local search are often obtained ...
Sarah Müller +2 more
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Measuring Polarization in Online Debates
Applied Sciences, 2021 Social networks can be a very successful tool to engage users to discuss relevant topics for society. However, there are also some dangers that are associated with them, such as the emergence of polarization in online discussions.
Teresa Alsinet +3 more
doaj +1 more source
Approximate Local Search in Combinatorial Optimization [PDF]
SIAM Journal on Computing, 2003 Local search algorithms for combinatorial optimization problems are in general of pseudopolynomial running time and polynomial-time algorithms are often not known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of epsilon-local optimality and show that an epsilon-local optimum can be identified in time ...
James B. Orlin +2 more
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A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning
Mathematics, 2022 Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters.
Yanyan Fan +5 more
doaj +1 more source
Searching in the Forest for Local Bayesian Optimization
CoRR, 2021 Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO). Recent empirical experiments showed that the loss landscapes of HPO problems tend to be more benign than previously assumed, i.e.
Difan Deng, Marius Lindauer
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