International Journal of Swarm Intelligence
- Editor in Chief
- Dr. Jagdish Chand Bansal
- ISSN online
- ISSN print
- 2 issues per year
Swarm intelligence is a computational intelligence technique to solve complex real-world problems. It involves the study of collective behaviour of individuals in a population who interact locally with one another and with their environment in a decentralised control system. IJSI is a peer-reviewed international publication dedicated to reporting research and new developments in the multidisciplinary field of swarm intelligence.
Topics covered include
- Particle swarm optimisation
- Artificial bees and fireflies algorithm
- Bacterial foraging optimisation
- Ant colony optimisation
- Swarm robotics
- Artificial immune systems
- Glow worm swarm optimisation
- Hybridisation of swarm intelligence methods
- Numerical algorithms formulation and analysis
- Review and comparative studies of swarm intelligence techniques
- New methodologies inspired from learning behaviour of social insects
- Theory and practice of swarm intelligence methods in different domains
- Real-world problem solving using swarm intelligence methods
IJSI serves as a forum for facilitating and enhancing information-sharing among swarm intelligence researchers in the field, ranging from algorithm developments to real-world applications. Targeting researchers, academicians, students and engineers, this journal provides innovative findings in swarm intelligence, computational intelligence and their applications.
IJSI is aimed at professionals, academics, students, researchers and policy makers who are interested in the potential of swarm intelligence based techniques for solving problems. In particular, this includes people with background in computer science, mathematics, engineering and quantitative management science. IJSI may serve as a reference title for the courses like soft computing, computational intelligence or swarm intelligence.
IJSI publishes all types of original research work such as papers, review papers, technical reports, conference papers (if substantially extended) and book reviews which have not been published elsewhere. Special Issues devoted to important topics in computational intelligence will occasionally be published.
Editor in Chief
- Bansal, Jagdish Chand, South Asian University, India
- Deep, Kusum, Indian Institute of Technology Roorkee, India
- Simon, Dan, Cleveland State University, USA
- Clerc, Maurice, , France
- Sharma, Harish, Rajasthan Technological University, India
Honorary Advisory Board
- Deb, Kalyanmoy, Michigan State University, USA
- Deshmukh, S.G., ABV-Indian Institute of Information Technology and Management Gwalior, India
- Sanyal, Sugata, Tata Consultancy Services, India
- Suganthan, Ponnuthurai Nagaratnam, Nanyang Technological University, Singapore
- Verma, A.K., Indian Institute of Technology Bombay, India
- Vrahatis, Michael N., University of Patras, Greece
Editorial Board Members
- Acan, Adnan, Eastern Mediterranean University, Cyprus
- Akay, Bahriye Baþtürk, Erciyes University, Turkey
- Ali, Montaz, Witwatersrand University, South Africa
- Barbosa, Helio J.C., LNCC/MCT - Laboratório Nacional de Computação Científica , Brazil
- Blackwell, Tim, Goldsmiths, University of London , UK
- Chan, Jonathan H., King Mongkut's University of Technology Thonburi, Thailand
- Coello Coello, Carlos A., CINVESTAV-IPN, Mexico
- Das, Swagatam, Indian Statistical Institute (ISI), India
- Engelbrecht, Andries, University of Pretoria, South Africa
- Evers, George, USA
- Fernández Martínez, Juan Luis, University of Oviedo, Spain
- Formato, Richard A., USA
- Gao, Xiao-Zhi, University of Eastern Finland, Finland
- García Gonzalo, Maria Esperanza, University of Oviedo, Spain
- Geem, Zong Woo "Victor", Gachon University, South Korea
- Ghose, Debasish, Indian Institute of Science, India
- Gong, Wenyin, China University of Geosciences, China
- Kim, Joong Hoon, Korea University, South Korea
- Krishnamoorthy, Mohan, Monash University, Australia
- Lim, Meng-Hiot, Nanyang Technological University, Singapore
- Liu, Hongbo, Dalian Maritime University, China
- Monmarché, Nicolas, Université François Rabelais Tours, France
- Nagar, Atulya K., Liverpool Hope University, UK
- Omran, Mahamed G.H., Gulf University for Science and Technology, Kuwait
- Pan, Linqiang, Huazhong University of Science and Technology , China
- Parsopoulos, Konstantinos E., University of Ioannina, Greece
- Pavone, Mario, University of Catania, Italy
- Salhi, Said, University of Kent, UK
- Shrivastava, Vivek, Rajasthan Technical University, India
- Siarry, Patrick, Université de Paris 12, France
- Yang, Xin-She, Middlesex University, UK
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.
- All articles for this journal must be submitted using our online submissions system.
- View Author guidelines.
World wide woof
3 November, 2021
Researchers in India have demonstrated how a convolution neural network can be used to identify dog breeds from photographs. Writing in the International Journal of Swarm Intelligence, the team explains how they have trained their algorithm with more than 15 million images of dogs and used a model that could carry out 22,000 different object classifications on those good resolution images. The system can then correctly identify which of 133 breeds is represented by a new photograph of a dog presented to it with 98 percent accuracy [...]More details...