1,338 research outputs found
Com gestionem els recursos pesquers?
Investigadors de la UAB han participat en una sèrie d'estudis que tenien l'objectiu de trobar millors mètodes per gestionar els recursos pesquers. Actualment, els òrgans de gestió de la captura de peixos utilitzen Ãndexs que no tenen en compte les diferents estacions de l'any ni tampoc la indústria pesquera actual, que captura moltes espècies de peixos al mateix temps. Aquesta investigació proposa nous cà lculs que tenen en compte tant el moment de l'any en què es pesca com la captura de diferents espècies.Investigadores de la UAB han participado en una serie de estudios que tenÃan el objetivo de encontrar mejores métodos para gestionar los recursos pesqueros. Actualmente, los órganos de gestión de la captura de peces utilizan indices que no tienen en cuenta las diferentes estaciones del año ni tampoco la industria pesquera actual, que captura muchas especies de peces a la vez. Esta investigación propone nuevos cálculos que tienen en cuenta tanto el momento del año en que se pesca como la captura de diferentes especies
Hybrid approaches to optimization and machine learning methods: a systematic literature review
Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT—
Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz
Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the
Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/
MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio
Hybrid approaches to optimization and machine learning methods
This paper conducts a comprehensive literature
review concerning hybrid techniques that combine optimization
and machine learning approaches for clustering and classification
problems. The aim is to identify the potential benefits of
integrating these methods to address challenges in both fields.
The paper outlines optimization and machine learning methods
and provides a quantitative overview of publications since 1970.
Additionally, it offers a detailed review of recent advancements
in the last three years. The study includes a SWOT analysis of
the top ten most cited algorithms from the collected database,
examining their strengths and weaknesses as well as uncovering
opportunities and threats explored through hybrid approaches.
Through this research, the study highlights significant findings
in the realm of hybrid methods for clustering and classification,
showcasing how such integrations can enhance the shortcomings
of individual techniques.info:eu-repo/semantics/publishedVersio
Conceptual multi-agent system design for distributed scheduling systems
With the progressive increase in the complexity of dynamic environments, systems require an
evolutionary configuration and optimization to meet the increased demand. In this sense, any
change in the conditions of systems or products may require distributed scheduling and resource
allocation of more elementary services. Centralized approaches might fall into bottleneck issues,
becoming complex to adapt, especially in case of unexpected events. Thus, Multi-agent systems
(MAS) can extract their automatic and autonomous behaviour to enhance the task effort
distribution and support the scheduling decision-making. On the other hand, MAS is able to
obtain quick solutions, through cooperation and smart control by agents, empowered by their
coordination and interoperability. By leveraging an architecture that benefits of a collaboration
with distributed artificial intelligence, it is proposed an approach based on a conceptual MAS
design that allows distributed and intelligent management to promote technological innovation in
basic concepts of society for more sustainable in everyday applications for domains with
emerging needs, such as, manufacturing and healthcare scheduling systems.This work has been supported by FCT - Fundação para a Ciência e a
Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020.
Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019.info:eu-repo/semantics/publishedVersio
A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indices
First Online: 28 December 2023Clustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020, UIDB/05757/2020, UIDP/05757/2020 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021
Hybrid approaches to optimization and machine learning methods
This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), Algoritmi (UIDB/00319/2020) and SusTEC (LA/P/0007/2021). Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021
Automatic nurse allocation based on a population algorithm for home health care
The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.This work has been supported by COMPETE:POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the project UID/CEC/00319/2019
Multi-agent system specification for distributed scheduling in home health care
Nowadays, scheduling and allocation of resources and tasks becomes a huge and complex challenge to the most diverse industrial areas, markets, services and health. The problem with current scheduling systems is that their management is still done manually or using classical optimization methods (usually static, time-consuming) and centralized approaches. However, opportunities arise to decentralize solutions with smart systems, which enable the distribution of the computational effort, the flexibility of behaviours and the minimization of operating times and operational planning costs. The paper proposes the specification of a Multi-agent System (MAS) for the Home Health Care (HHC) scheduling and allocation. The MAS technology enables the scheduling of intelligent behaviours and functionalities based on the interaction of agents, and allows the evolution of current strategies and algorithms, as it can guarantee the fast response to condition changes, flexibility and responsiveness in existing planning systems. An experimental HHC case study was considered to test the feasibility and effectiveness of the proposed MAS approach, the results demonstrating promising qualitative and quantitative indicators regarding the efficiency and responsiveness of the HHC scheduling.This work has been supported by FCT—Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019
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