Communications in Combinatorics and Optimization

Scope & Guideline

Catalyzing Knowledge Exchange in Combinatorial Science

Introduction

Explore the comprehensive scope of Communications in Combinatorics and Optimization through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Communications in Combinatorics and Optimization in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2538-2128
PublisherAZARBAIJAN SHAHID MADANI UNIV
Support Open AccessNo
CountryIran
TypeJournal
Convergefrom 2016 to 2024
AbbreviationCOMMUN COMB OPTIM / Commun. Combinatorics Optimization
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address35 Km Tabriz-Maragheh Road, P.O.B: 53714-161, TABRIZ 00000, IRAN

Aims and Scopes

The journal 'Communications in Combinatorics and Optimization' aims to publish high-quality research that advances the fields of combinatorial mathematics and optimization. Its scope includes theoretical developments, algorithmic innovations, and applications of combinatorial and optimization techniques across various domains.
  1. Combinatorial Optimization:
    Focus on the study of optimization problems where the objective is to find the best solution from a finite set of solutions, using combinatorial structures.
  2. Graph Theory:
    Investigation of properties and applications of graphs, including domination, coloring, and spectral graph theory, which are prevalent themes in the published works.
  3. Algorithm Development:
    Emphasis on the design and analysis of algorithms for solving complex combinatorial and optimization problems, including approximation algorithms and computational complexity.
  4. Mathematical Modeling:
    Utilization of mathematical models to represent and solve real-world problems, particularly in network design, resource allocation, and scheduling.
  5. Interdisciplinary Applications:
    Application of combinatorial and optimization techniques to fields such as computer science, operations research, biology, and social sciences.
The journal has recently seen a rise in interest in several emerging themes that reflect the evolving landscape of combinatorial mathematics and optimization. These trends indicate areas of growing importance and research viability.
  1. Multi-Objective Optimization:
    Increasing focus on problems involving multiple objectives, where trade-offs between conflicting goals are analyzed, is becoming a significant area of research.
  2. Graph Spectral Theory:
    A surge in publications related to spectral graph theory, particularly concerning graph energies and indices, highlights a growing interest in the relationship between graph properties and their spectra.
  3. Algorithmic Complexity and Efficiency:
    A notable trend towards exploring the complexities and efficiencies of algorithms, particularly in the context of dominations and graph operations, showcases the need for robust computational methods.
  4. Applications in Network Theory:
    Research focusing on the application of combinatorial and optimization techniques to network structures, including traffic models and resource allocation in networks, is on the rise.
  5. Fuzzy and Interval-Valued Graphs:
    Emerging interest in the study of fuzzy graphs and interval-valued graphs reflects a broader trend towards incorporating uncertainty and flexibility into combinatorial structures.

Declining or Waning

While the journal continues to thrive in various domains, certain themes appear to be losing traction in recent publications. These waning scopes indicate a shift in focus among researchers.
  1. Classical Graph Theory:
    Topics centered on traditional aspects of graph theory, such as basic graph properties and simpler algorithms, have seen a decline as more complex and applied theories gain prominence.
  2. Elementary Combinatorial Results:
    Basic combinatorial results and theorems that do not involve advanced techniques or applications are becoming less frequent, reflecting a trend toward more sophisticated and applicable research.
  3. Static Optimization Problems:
    Research focused on static or single-objective optimization problems is less common, as the trend shifts towards dynamic, multi-objective, and real-time optimization approaches.

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