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  1. Article ; Online: Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

    Martínez-Álvarez, F / Asencio-Cortés, G / Torres, J F / Gutiérrez-Avilés, D / Melgar-García, L / Pérez-Chacón, R / Rubio-Escudero, C / Riquelme, J C / Troncoso, A

    Big data

    2020  Volume 8, Issue 4, Page(s) 308–322

    Abstract: ... The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First ... This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and ... traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible ...

    Abstract This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.
    MeSH term(s) Algorithms ; Betacoronavirus/isolation & purification ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/transmission ; Coronavirus Infections/virology ; Disease Outbreaks ; Heuristics ; Humans ; Models, Theoretical ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/transmission ; Pneumonia, Viral/virology ; Probability ; Quarantine ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-07-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2167-647X
    ISSN (online) 2167-647X
    DOI 10.1089/big.2020.0051
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

    Martínez-Álvarez, F / Asencio-Cortés, G / Torres, J F / Gutiérrez-Avilés, D / Melgar-García, L / Pérez-Chacón, R / Rubio-Escudero, C / Riquelme, J C / Troncoso, A

    Big Data

    Abstract: ... The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First ... This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and ... traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible ...

    Abstract This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #679594
    Database COVID19

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  3. Article: Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model

    Mart'inez-'Alvarez, F. / Asencio-Cort'es, G. / Torres, J. F. / Guti'errez-Avil'es, D. / Melgar-Garc'ia, L. / P'erez-Chac'on, R. / Rubio-Escudero, C. / Riquelme, J. C. / Troncoso, A.

    Abstract: ... as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major ... A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads ... the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during ...

    Abstract A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability of ending after several iterations, without setting this value either. Infected population initially grows at an exponential rate but after some iterations, when considering social isolation measures and the high number recovered and dead people, the number of infected people starts decreasing in subsequent iterations. Furthermore, a parallel multi-virus version is proposed in which several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  4. Article: Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model

    F. Mart'inez-'Alvarez / G. Asencio-Cort'es / J. Torres F. / D. Guti'errez-Avil'es / L. Melgar-Garc'ia / R. P'erez-Chac'on / C. Rubio-Escudero / J. Riquelme C. / A. Troncoso

    Abstract: ... as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major ... A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads ... the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during ...

    Abstract A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability of ending after several iterations, without setting this value either. Infected population initially grows at an exponential rate but after some iterations, when considering social isolation measures and the high number recovered and dead people, the number of infected people starts decreasing in subsequent iterations. Furthermore, a parallel multi-virus version is proposed in which several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.
    Keywords covid19
    Publisher arxiv
    Document type Article
    Database COVID19

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  5. Article ; Online: Coronavirus Optimization Algorithm

    Martínez-Álvarez, F. / Asencio-Cortés, G. / Torres, J. F. / Gutiérrez-Avilés, D. / Melgar-García, L. / Pérez-Chacón, R. / Rubio-Escudero, C. / Riquelme, J. C. / Troncoso, A.

    Big Data

    A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

    2020  Volume 8, Issue 4, Page(s) 308–322

    Keywords Information Systems and Management ; Information Systems ; Computer Science Applications ; covid19
    Language English
    Publisher Mary Ann Liebert Inc
    Publishing country us
    Document type Article ; Online
    ISSN 2167-6461
    DOI 10.1089/big.2020.0051
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: Coronavirus Optimization Algorithm

    Martínez-Álvarez, F. / Asencio-Cortés, G. / Torres, J. F. / Gutiérrez-Avilés, D. / Melgar-García, L. / Pérez-Chacón, R. / Rubio-Escudero, C. / Riquelme, J. C. / Troncoso, A.

    A bioinspired metaheuristic based on the COVID-19 propagation model

    2020  

    Abstract: ... as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major ... A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads ... the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during ...

    Abstract A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability of ending after several iterations, without setting this value either. Infected population initially grows at an exponential rate but after some iterations, when considering social isolation measures and the high number recovered and dead people, the number of infected people starts decreasing in subsequent iterations. Furthermore, a parallel multi-virus version is proposed in which several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.

    Comment: 30 pages, 4 figures
    Keywords Computer Science - Artificial Intelligence ; covid19
    Subject code 006
    Publishing date 2020-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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