Natural Computing

Aims & Scope

The journal is soliciting papers on all aspects of natural computing.

Because of the interdisciplinary character of the journal a special effort will be made to solicit survey, review, and tutorial papers which would make research trends in a given subarea more accessible to the broad audience of the journal.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.6
2024	1.70

SJR (SCImago Journal Rank)

Year	Value
2024	0.456

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	50

Journal Rank

Year	Value
2024	12506

Journal Citation Indicator

Year	Value
2024	321

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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
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, 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.

• Evolution strategies – A comprehensive introduction

  Citation: 2136

  Authors: Hans-Georg, Hans-Paul

• Recent approaches to global optimization problems through Particle Swarm Optimization

  Citation: 1046

  Authors: K.E., M.N.

• BGSA: binary gravitational search algorithm

  Citation: 606

  Authors: Esmat, Hossein, Saeid

• A survey on metaheuristics for stochastic combinatorial optimization

  Citation: 560

  Authors: Leonora, Marco, Luca Maria, Walter J.

• A tutorial on multiobjective optimization: fundamentals and evolutionary methods

  Citation: 523

  Authors: Michael T. M., André H.

• A review of particle swarm optimization. Part I: background and development

  Citation: 508

  Authors: Alec, Jonathan, Chukwudi

• A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications

  Citation: 391

  Authors: Alec, Jonathan, Chukwudi

• Learning probability distributions in continuous evolutionary algorithms – a comparative review

  Citation: 209

  Authors: Stefan, Sibylle D., Nikolaus, Dirk, Jiri, Petros

• A study of drift analysis for estimating computation time of evolutionary algorithms

  Citation: 206

  Authors: Jun, Xin

• Computation with finite stochastic chemical reaction networks

  Citation: 205

  Authors: David, Matthew, Erik, Jehoshua

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