Soft Computing

Aims & Scope

Soft Computing provides rapid dissemination of important results in soft computing foundations, methodologies and applications.

It encourages the integration of soft computing theoretical and practical results into both everyday and advanced applications.

The journal aims to connect the ideas and techniques of soft computing with other disciplines.

Soft Computing is dedicated to system solutions based on soft computing paradigms.

It provides rapid dissemination of important results in soft computing, a fusion of research in evolutionary algorithms, genetic programming, swarm intelligence, neural science, neural net systems, fuzzy set theory, fuzzy systems, Bayesian networks, chaos theory, chaotic systems.

By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications.

As a result, the journal is an international forum for all scientists engaged in research and development in this fast growing field.

View Aims & Scope

---

Metrics & Ranking

Impact Factor

Year	Value
2025	2.5
2024	3.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.674

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	120

Journal Rank

Year	Value
2024	8488

Journal Citation Indicator

Year	Value
2024	12845

---

---

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

---

Subjects & Keywords

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

---

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.

• Particle swarm optimization algorithm: an overview

  Citation: 2466

  Authors: Dongshu, Dapei, Lei

• Butterfly optimization algorithm: a novel approach for global optimization

  Citation: 1407

  Authors: Sankalap, Satvir

• KEEL: a software tool to assess evolutionary algorithms for data mining problems

  Citation: 1206

  Authors: J., L., S., M. J., S., J. M., J., C., J., V. M., J. C., F.

• A comprehensive survey of fitness approximation in evolutionary computation

  Citation: 963

  Authors: Y.

• Deep packet: a novel approach for encrypted traffic classification using deep learning

  Citation: 745

  Authors: Mohammad, Mahdi, Ramin, Mohammdsadegh

• A Fuzzy Adaptive Differential Evolution Algorithm

  Citation: 629

  Authors: J., J.

• Optimizing connection weights in neural networks using the whale optimization algorithm

  Citation: 609

  Authors: Ibrahim, Hossam, Seyedali

• A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

  Citation: 561

  Authors: S., A., J., F.

• Soft sets combined with fuzzy sets and rough sets: a tentative approach

  Citation: 509

  Authors: Feng, Changxing, B., M. Irfan

• Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

  Citation: 500

  Authors: Pradeep, Ardhendu, Marcello, Ella, Morteza, Francesco, Yonghuai

---