Computer Assisted Methods in Engineering and Science (CAMES) is a referred international journal, published quarterly, indexed by Scopus and EBSCO, providing a scientific exchange forum and an authoritative source of information in the field of computational sciences and related areas of applied engineering. The objective of the journal is to support researchers and practitioners by offering them the means facilitating access to the newest research results reported by leading experts in the field, publication of own contributions, and dissemination of information relevant to the scope of the journal.

Papers published in the journal will fall largely into three main categories:

  • Contributions presenting new research methods of mathematical modeling and computer simulations in engineering and applied sciences, including traditional areas such as solid and structural mechanics, material science, fluid dynamics, acoustics and electromagnetics but going beyond them to account for application relevant issues in physics, chemistry, biology and mathematics, scientific computing, large scale optimization, intelligent systems as well as in multi-scale and multi-physics problems.
  • Articles describing novel applications of computational techniques supporting engineering practice and education in areas like mechanical, aerospace, civil, naval, software, chemical and architectural engineering, materials science as well as demonstrations of their practical use in solving real life problems.
  • State-of-the-art tutorials, providing the readership with a guidance on important research directions as observed in the current world literature on computer assisted methods in engineering and sciences.

The journal will also publish book reviews and information on activities of the European Community on Computational Methods in Applied Sciences (ECCOMAS).

Print ISSN: 2299-3649

Journal Metrics

MNiSW (2019): 20   |   CiteScore 2019: 0.7   |   SJR 2019: 0.150   |   SNIP 2019: 0.201


Call for Papers on Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems


With the rising complexity of problems, optimization has an inherent element of uncertainty that is expressed in many formal ways and this necessitates the need to develop novel techniques to create optimum and interpretable solutions for industrial operations. This special issue aims to discuss the intelligent optimization algorithms including particle swarm optimization, genetic algorithm, differential evolution, distribution estimation, ant colony optimization, artificial bee colony, greedy optimization, and many others, which have been successfully applied to different distributed scheduling problems as well as the real-time industrial systems.

Read more about Call for Papers on Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems