GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • V.M. Glushkov Institute of Cybernetics  (2)
Material
Publisher
  • V.M. Glushkov Institute of Cybernetics  (2)
Person/Organisation
Language
Years
  • 1
    Online Resource
    Online Resource
    The Efficiency of Discrete Optimization Algorithm Portfolios
    V.M. Glushkov Institute of Cybernetics ; 2021
    In:  Cybernetics and Computer Technologies , No. 2 ( 2021-6-30), p. 5-12
    Details
    In: Cybernetics and Computer Technologies, V.M. Glushkov Institute of Cybernetics, , No. 2 ( 2021-6-30), p. 5-12
    Abstract: Introduction. Solving large-scale discrete optimization problems requires the processing of large-scale data in a reasonable time. Efficient solving is only possible by using multiprocessor computer systems. However, it is a daunting challenge to adapt existing optimization algorithms to get all the benefits of these parallel computing systems. The available computational resources are ineffective without efficient and scalable parallel methods. In this connection, the algorithm unions (portfolios and teams) play a crucial role in the parallel processing of discrete optimization problems. The purpose. The purpose of this paper is to research the efficiency of the algorithm portfolios by solving the weighted max-cut problem. The research is carried out in two stages using stochastic local search algorithms. Results. In this paper, we investigate homogeneous and non-homogeneous algorithm portfolios. We developed the homogeneous portfolios of two stochastic local optimization algorithms for the weighted max-cut problem, which has numerous applications. The results confirm the advantages of the proposed methods. Conclusions. Algorithm portfolios could be used to solve well-known discrete optimization problems of unprecedented scale and significantly improve their solving time. Further, we propose using communication between algorithms, namely teams and portfolios of algorithm teams. The algorithms in a team communicate with each other to boost overall performance. It is supposed that algorithm communication allows enhancing the best features of the developed algorithms and would improve the computational times and solution quality. The underlying algorithms should be able to utilize relevant data that is being communicated effectively to achieve any computational benefit from communication. Keywords: Discrete optimization, algorithm portfolios, computational experiment.
    Type of Medium: Online Resource
    ISSN: 2707-451X , 2707-4501
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.34229/2707-451X.21.2
    DOI: 10.34229/2707-451X
    DOI: 10.34229/2707-451X.21.2.1
    Language: English
    Publisher: V.M. Glushkov Institute of Cybernetics
    Publication Date: 2021
    detail.hit.zdb_id: 3072204-4
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Application of the Global Equilibrium Search Method for Solving Boolean Programming Problems
    V.M. Glushkov Institute of Cybernetics ; 2023
    In:  Cybernetics and Computer Technologies , No. 2 ( 2023-7-28), p. 11-22
    Details
    In: Cybernetics and Computer Technologies, V.M. Glushkov Institute of Cybernetics, , No. 2 ( 2023-7-28), p. 11-22
    Abstract: Introduction. The significance of methods and algorithms for solving discrete optimization problems in mathematical supporting computer technologies of diverse levels and objectives is increasing. Consequently, the efficacy of discrete optimization methods deserves particular attention, as it drives the advancement of techniques capable of solving complex real-world problems. This paper introduces the Global Equilibrium Search (GES) method as a highly effective approach for solving Boolean programming problems, thus contributing to the field's progress and applicability. Purpose. We describe the successful application of the approximate probabilistic GES method for effectively solving various Boolean programming problems. Results. This paper explores the application of sequential GES algorithms for solving Boolean linear, Boolean quadratic programming, and other related problems with their specific characteristics. In our study, we conducted a comparative analysis to assess the effectiveness of GES algorithms by evaluating them against state-of-the-art approaches. Additionally, to parallelize the optimization process for discrete programming problems, we introduced algorithm unions, specifically portfolios, and teams. The efficiency of GES algorithm portfolios and teams is investigated by solving the maximum weighted graph cut problem, with subsequent comparisons to identify distinctions between them. Conclusions. Based on the accumulated experience of applying GES algorithms and their modifications to solve discrete optimization problems, this study establishes the GES method as the leading approximate approach for Boolean programming. The results demonstrate the GES algorithm unions experience a significant boost in the optimization process speed, whereas algorithm teams demonstrate higher efficiency. Keywords: global equilibrium search method, Boolean programming problems, experimental studies, algorithm efficiency, algorithm unions (portfolios and teams).
    Type of Medium: Online Resource
    ISSN: 2707-451X , 2707-4501
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.34229/2707-451X.23.2
    DOI: 10.34229/2707-451X
    DOI: 10.34229/2707-451X.23.2.2
    Language: English
    Publisher: V.M. Glushkov Institute of Cybernetics
    Publication Date: 2023
    detail.hit.zdb_id: 3072204-4
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...