Journal Information
Journal of Computational Science
http://www.journals.elsevier.com/journal-of-computational-science/
Impact Factor:
1.748
Publisher:
ELSEVIER
ISSN:
1877-7503
Viewed:
4224
Tracked:
3

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Call For Papers
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.

Computational science typically unifies three distinct elements:

• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).

The Journal of Computational Science aims to be an international platform to exchange novel research results in simulation based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices.

The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.
Last updated by Dou Sun in 2017-08-05
Special Issues
Special Issue on Soft Computing for Swarm Robotics: New Trends and Applications
Submission Date: 2018-08-01

Swarm Intelligence (SI) refers to the complex collective behavior of self-organized and decentralized systems, typically composed of a (spatially distributed and often large) population of individuals, or agents. These agents interact among them and with the environment in different but simple and local ways, coordinating their actions, and making the swarm inherently robust, effective, and flexible. A plethora of application scenarios have hitherto resorted to SI when addressing optimization, inference and prediction tasks. Among them, Swarm robotics (SR) refers to the application of SI methods to scenarios where the population of agents is embodied by physical or simulated robotic devices. The focus of SR is to thoroughly analyze how a swarm comprised of relatively simple physically embodied robots can be controlled to collectively accomplish different kind of goals that are out of the common capabilities of a single robot. Algorithms and methods relying on SR have been so far exceled over a wide range of complex real-world problems, such as localization, mining, disaster rescue missions, agricultural foraging or scenery mapping problems. The interests in SR form a popular topic that lays at the core of many research activities and contributions in the literature. This special issue aims at disseminating the latest findings and research achievements in the areas of SI and SR, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to this special issue. Topics of interest include, but are not limited to: - Recent advances on Soft Computing methods for Robotics, with an emphasis on those inspired by processes and behaviors typically observed in Nature - Novel applications of Swarm Robotics, with a priority on real-world scenarios. - Hybridization of Swarm Intelligence techniques, with applications to robotics or autonomous complex systems. - New synergies between Swarm Intelligence and Swarm Robotics. - Coordination and control of Swarm Robotic Systems. - Adaptive Soft Computing methods. - Applications of Swarm Intelligence for collaborative positioning and route optimization in robotic swarms. - Distributed inference in Swarm Robotics. - Self-organization in robotics enabled by Swarm Intelligence. - Distributed Swarm Robotic systems.
Last updated by Dou Sun in 2017-11-04
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