Hybrid meta-heuristic optimization algorithms for time-domain-constrained data clustering

García, María Luz López; García-Ródenas, Ricardo; Gómez, A. González

doi:10.1016/j.asoc.2014.06.046

Cited by 16 publications

(2 citation statements)

References 38 publications

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“…The hybridized algorithm continues to run the A L method with good exploitative properties for a fixed number of iterations starting from the best current solution. This hybridization strategy has been applied to solve train time-tabling problems (Espinosa-Aranda et al, 2015) and segmentation of temporal series (López-García et al, 2014).…”

Section: Hybridization Of Metaheuristicsmentioning

confidence: 99%

A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs

García-Ródenas

García-García

López–Fidalgo

et al. 2020

Computational Statistics & Data Analysis

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Several common general purpose optimization algorithms are compared for finding A-and D-optimal designs for different types of statistical models of varying complexity, including high dimensional models with five and more factors. The algorithms of interest include exact methods, such as the interior point method, the Nelder-Mead method, the active set method, the sequential quadratic programming, and metaheuristic algorithms, such as particle swarm optimization, simulated annealing and genetic algorithms. Several simulations are performed, which provide general recommendations on the utility and performance of each method, including hybridized versions of metaheuristic algorithms for finding optimal experimental designs. A key result is that general-purpose optimization algorithms, both exact methods and metaheuristic algorithms, perform well for finding optimal approximate experimental designs.

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Section: Hybridization Of Metaheuristicsmentioning

confidence: 99%

A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs

García-Ródenas

García-García

López–Fidalgo

et al. 2020

Computational Statistics & Data Analysis

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“…This paper considers the NelderMead method (NM, see Nelder and Mead (1965)), Standard Particle Swarm Optimization (SPSO, see Zambrano-Bigiarini et al (2013)) and a hibridization of both methods (SPSO+NM, see Espinosa-Aranda et al (2013)). The SPSO+NM algorithm has demonstrated good performance on various unconstrained benchmark functions (see ) and it has also been applied successfully to cluster analysis (Lopez-García et al (2014)). The HSR-TTP model is computationally demanding and it requires parallelization strategies to obtain satisfactory solutions in real instances.…”

Section: Metaheuristic Algorithmmentioning

confidence: 98%

High-speed railway scheduling based on user preferences

Espinosa-Aranda

García-Ródenas

Ramírez-Flores

et al. 2015

European Journal of Operational Research

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An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times

Chen

Niu

et al. 2022

Cluster Comput

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Surgical case scheduling is a key issue in the field of medician, which is a challenging work because of the difficulty in assigning resources to patients. This study regards the surgical case scheduling problem as a flexible job shop scheduling problem (FJSP). Considering the switching and preparation time of patients in different stage, an improved multi-objective imperialist competitive algorithm (IMOICA), which adopts the non-dominant sorting method, is proposed to optimize the whole scheduling. First, the social hierarchy strategy is developed to initialize the empire. Then, to enhance the global search ability of the algorithm, the concept of attraction and repulsion (AR) is introduced into the assimilation strategy. Moreover, to increase the diversity of the population, the revolution strategy is utilized. Finally, the variable neighborhood search (VNS) strategy is embedded to improve its exploitation capacity further. Experiments show that scheduling in advance saves time and cost, and IMOICA can solve the surgical case scheduling problem studied efficiently.

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Hybrid meta-heuristic optimization algorithms for time-domain-constrained data clustering

Cited by 16 publications

References 38 publications

A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs

A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs

High-speed railway scheduling based on user preferences

An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times

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