Journal of Heuristics

Aims & Scope

The Journal of Heuristics provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly.

It fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems.

It considers the importance of theoretical, empirical, and experimental work related to the development of heuristics.

The journal presents practical applications, theoretical developments, decision analysis models that consider issues of rational decision making with limited information, artificial intelligence-based heuristics applied to a wide variety of problems, learning paradigms, and computational experimentation.

Officially cited as: J Heuristics Provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly.

Fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems.

Considers the importance of theoretical, empirical, and experimental work related to the development of heuristics.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.4
2024	1.10

Journal Rank

Year	Value
2024	12452

Journal Citation Indicator

Year	Value
2024	159

SJR (SCImago Journal Rank)

Year	Value
2024	0.458

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	75

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

  Citation: 1446

  Authors: Salvador, Daniel, Manuel, Francisco

• Probabilistic diversification and intensification in local search for vehicle routing

  Citation: 660

  Authors: Yves, Éric D.

• A Taxonomy of Hybrid Metaheuristics

  Citation: 655

  Authors: E.-G.

• A Genetic Algorithm for the Multidimensional Knapsack Problem

  Citation: 622

  Authors: P.C., J.E.

• Biased random-key genetic algorithms for combinatorial optimization

  Citation: 450

  Authors: José Fernando, Mauricio G. C.

• A hybrid grouping genetic algorithm for bin packing

  Citation: 444

  Authors: Emanuel

• A Tabu-Search Hyperheuristic for Timetabling and Rostering

  Citation: 385

  Authors: E.K., G., E.

• Designing and reporting on computational experiments with heuristic methods

  Citation: 312

  Authors: Richard S., Bruce L., James P., Mauricio G. C., William R.

• Testing heuristics: We have it all wrong

  Citation: 284

  Authors: J. N.

• Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial

  Citation: 258

  Authors: Ronald L., Reha

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