Swarm and Evolutionary Computation - Impact Factor

Swarm and Evolutionary Computation - Elsevier | 2025 Impact Factor:9.6 | Cite Score:12.4 | Q1

Swarm and Evolutionary Computation Journal

Impact Factor and Journal Rank of Swarm and Evolutionary Computation

About: The Swarm and Evolutionary Computation journal serves as a prominent platform for researchers, academics, and practitioners to publish and access high-quality research at the intersection of swarm intelligence and evolutionary computation. It provides a valuable platform for the exchange of ideas, collaboration, and advancement of research in swarm intelligence and evolutionary computation, fostering innovation and interdisciplinary dialogue in these exciting fields
Key Focus Areas:
Swarm Intelligence: Research in this area explores the collective behaviors observed in natural systems and seeks to develop algorithms and models inspired by these phenomena. Topics may include ant colony optimization, particle swarm optimization, bee colony optimization, bacterial foraging optimization, and other bio-inspired approaches.
Evolutionary Computation: This field draws inspiration from the principles of natural selection and genetic mechanisms to solve complex optimization and search problems. Genetic algorithms, genetic programming, evolutionary strategies, and other evolutionary computation techniques are explored, along with their variants and hybridizations.
Editorial Policies: The journal maintains rigorous editorial standards, including double-blind peer review, to ensure the quality and integrity of published research. It adheres to ethical guidelines and promotes transparency, reproducibility, and open access to scientific knowledge.
Significance: This journal plays a vital role in advancing research, fostering collaboration, and promoting the adoption of innovative computational techniques with broad applications and significant implications for science and technology.

Journal Home: Journal Homepage

Editor-in-Chief: Ling Wang

Scope: The Swarm and Evolutionary Computation journal focuses on research at the intersection of two dynamic and interdisciplinary fields: swarm intelligence and evolutionary computation.
Here is an overview of the scope of the journal:
Swarm Intelligence: This field explores the collective behavior of decentralized, self-organized systems inspired by natural phenomena such as ant colonies, bird flocks, and fish schools. Swarm intelligence algorithms typically involve multiple agents interacting locally with each other and their environment to achieve complex, global behavior. The journal welcomes research on various aspects of swarm intelligence, including algorithm design, analysis, optimization, modeling, and applications across diverse domains.
Evolutionary Computation: Evolutionary computation is rooted in principles of natural selection and genetics, leveraging evolutionary processes like mutation, recombination, and selection to solve complex optimization and search problems. The journal covers a wide range of topics within evolutionary computation, including but not limited to genetic algorithms, genetic programming, evolutionary strategies, differential evolution, and other evolutionary techniques. Research on theoretical foundations, algorithm development, empirical studies, and practical applications of evolutionary computation is encouraged.
Hybrid Approaches: The journal also welcomes research on hybrid approaches that integrate swarm intelligence and evolutionary computation techniques. These hybrid methods often leverage the complementary strengths of both paradigms to tackle complex optimization tasks more effectively. Contributions focusing on the development, analysis, and application of hybrid swarm and evolutionary algorithms are within the scope of the journal.

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