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Axioms
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Combinatorial Optimization & Applications
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Combinatorial Optimization & Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5022

Special Issue Editors

Dr. Hafiz Munsub Ali

E-Mail Website
Guest Editor
Computer Science Department, State University of New York at Binghamton, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: combinatorial optimization; network planning; resource planning and placement; evolutionary computation; machine learning; computational intelligence
Dr. Syed Ahmad Chan Bukhari

E-Mail Website
Guest Editor
Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, Queens, NY, USA
Interests: healthcare informatics; healthcare data standards and vocabularies; database management systems for healthcare; software development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your original research work to the Special Issue “Combinatorial Optimization & Applications” in Axioms. Combinatorics is a branch of mathematics focused on the enumeration, combination, and permutation of sets of elements and the mathematical relations that characterize their properties. More formally, this concept is known as combinatorial optimization, and has applications in almost every research domain involving computationally challenging problems. Generally, the goal of combinatorial optimization is to find an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. The most studied combinatorial optimization problems are the travelling salesman problem, knapsack problem, and bin packing.  Most of the known exact algorithms used to solve these types of problems require worst-case exponential time, which is not conducive to solving real-world problems. As a feasible approach, researchers are developing approximate algorithms that can generate superior-quality solutions in less time. This Special Issue aims to publish papers on topics including but not limited to combinatorial optimization models and algorithms able to solve real-world issues for existing and upcoming applications.

Dr. Hafiz Munsub Ali
Dr. Syed Ahmad Chan Bukhari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • combinatorial optimization issues in machine learning techniques
  • combinatorial optimization issues in artificial intelligence
  • combinatorial optimization issues in surrogate-assisted computing
  • applications of evolutionary computation
  • applications of approximate algorithms
  • modelling combinatorial optimization issues in healthcare
  • modelling combinatorial optimization issues in smart applications
  • planning a network infrastructure
  • placing virtual machines in datacentres
  • optimizing network resources
  • optimizing operating power in network
  • optimize routing in network
  • real-world scheduling problems
  • similar real-world applications, etc.

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Published Papers (2 papers)

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Research

17 pages, 15416 KiB  
Article
Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood
by Jiaxuan Zhang and Youlong Yang
Axioms 2023, 12(5), 425; https://doi.org/10.3390/axioms12050425 - 26 Apr 2023
Cited by 5 | Viewed by 2924
Abstract
Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorithms and distance-based algorithms. [...] Read more.
Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorithms and distance-based algorithms. However, both approaches have some drawbacks. The former struggles to handle low-density modes, while the latter cannot detect local outliers. Moreover, the outlier detection algorithm is very sensitive to parameter settings. This paper proposes a new two-parameter outlier detection (TPOD) algorithm. The method proposed in this paper does not need to manually define the number of neighbors, and the introduction of relative distance can also solve the problem of low density and further accurately detect outliers. This is a combinatorial optimization problem. Firstly, the number of natural neighbors is iteratively calculated, and then the local density of the target object is calculated by adaptive kernel density estimation. Secondly, the relative distance of the target points is computed through natural neighbors. Finally, these two parameters are combined to obtain the outlier factor. This eliminates the influence of parameters that require users to determine the number of outliers themselves, namely, the top-n effect. Two synthetic datasets and 17 real datasets were used to test the effectiveness of this method; a comparison with another five algorithms is also provided. The AUC value and F1 score on multiple datasets are higher than other algorithms, indicating that outliers can be found accurately, which proves that the algorithm is effective. Full article
(This article belongs to the Special Issue Combinatorial Optimization & Applications)
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20 pages, 1047 KiB  
Article
Learning the Parameters of ELECTRE-Based Primal-Dual Sorting Methods that Use Either Characteristic or Limiting Profiles
by Jorge Navarro, Eduardo Fernández, Efrain Solares, Abril Flores and Raymundo Díaz
Axioms 2023, 12(3), 294; https://doi.org/10.3390/axioms12030294 - 11 Mar 2023
Viewed by 1387
Abstract
Two multicriteria-sorting methods that generalize the relational paradigm have been recently presented in the literature. One uses objects representative of classes, the other uses objects in the limiting boundaries of classes; both can use either a reflexive or an asymmetric preference relation. However, [...] Read more.
Two multicriteria-sorting methods that generalize the relational paradigm have been recently presented in the literature. One uses objects representative of classes, the other uses objects in the limiting boundaries of classes; both can use either a reflexive or an asymmetric preference relation. However, defining the parameters of relation-based methods is not straightforward. The present work operationalizes those methods with a methodology that takes examples provided by the decision-maker and, using an accuracy measure that specifically fits the characteristics of the methods, exploits an evolutionary algorithm to determine the parameters that best reproduce such examples. The assessment of the proposal showed that (i) it can achieve considerably high levels of out-of-sample effectiveness with only a few decision examples; (ii) the inference process is more effective learning the parameters of the method based on representative objects; (iii) it tends to be more effective with a reflexive relation; (iv) the effectiveness decreases while increasing the number of classes, which is not always the case when increasing the number of criteria. Theoretical properties of the proposed methodology will be investigated in future works. Full article
(This article belongs to the Special Issue Combinatorial Optimization & Applications)
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