| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 677 | 19 | 21 | 8 |
The journal focuses largely on the fields of Artificial intelligence, Swarm behaviour, Robot, Swarm robotics and Mathematical optimization. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Swarm intelligence, Machine learning and Cognitive science. The study on Swarm intelligence presented is investigated in conjunction with research in Stigmergy.
While Swarm behaviour is the focus of the journal, it also provided insights into the studies of Robustness (computer science), Control theory and Foraging. The tackled Robot research is interrelated with Distributed computing which concerns subjects like Simulation. Ant robotics and Self-organization are some topics wherein Swarm robotics research discussed in Swarm Intelligence have an impact.
Mathematical optimization studies presented include Particle swarm optimization, Metaheuristic, Optimization problem, Evolutionary algorithm and Multi-objective optimization. In particular, the Particle swarm optimization works presented emphasize discussions on Multi-swarm optimization. The studies on Metaheuristic discussed can also contribute to research in the domains of Continuous optimization and Combinatorial optimization.
The published articles focus largely on the fields of Artificial intelligence, Robot, Swarm robotics, Swarm behaviour and Swarm intelligence. The most cited papers focus on Swarm intelligence but the discussions also offer insight into other areas such as Multi-swarm optimization, Ant colony optimization algorithms, Metaheuristic and Stigmergy. The published articles facilitate discussions on Artificial life that incorporate concepts from other fields like Artificial neural network, Nonlinear system and Psoa.
The journal mostly deals with topics like Artificial intelligence, Swarm behaviour, Robot, Group decision-making and Computer communication networks. The work on Artificial intelligence tackled in Swarm Intelligence brings together disciplines like Swarm intelligence and Machine learning. It tackles research works in Swarm intelligence as well as Virtual machine.
Swarm Intelligence addresses concerns in Swarm behaviour which are intertwined with other disciplines, such as Evolutionary algorithm, Foraging and Reinforcement learning. The journal explores issues in Robot which can be linked to other research areas like Distributed algorithm, Multi-agent system and Pairwise comparison. In addition to Swarm robotics research, Swarm Intelligence aims to explore topics under Bayesian probability and Communication complexity.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Swarm Intelligence (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Swarm Intelligence (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 16.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 26.67% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 33.33% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
With the increasing research and new discoveries in the field of Swarm Intelligence, there emerges a plethora of career opportunities for young aspirants and researchers. Positions like data scientists, AI developers and algorithm specialists are becoming more prevalent in various industries, especially in the tech world. For those who wish to explore this path, a strong foundation in mathematics, computer science, and artificial intelligence is critical. Additionally, experience in machine learning, cognitive science, and related aspects of artificial intelligence can be beneficial.
One such professional pathway in the realm of Swarm Intelligence is becoming a Preschool Teacher Assistant in an educational institution that emphasizes learning through innovative AI and robotics programs. These schools often employ cutting-edge Swarm Intelligence concepts to design engaging, interactive educational content for young learners. The role may involve utilizing the principles of Swarm Intelligence to coordinate and manage digital classrooms, develop age-appropriate curriculums integrated with AI and robotics, and foster an environment of experiential learning and creativity.
To learn more about the specific requirements and qualifications for such a role, consider visiting the guide on how to become a preschool teacher assistant in west virginia. This resource provides comprehensive information about the steps to follow, the education and experience needed, and the long-term career growth prospects in this field.
The escalating developments in Swarm Intelligence brings forth countless possibilities in the professional world. The key to making headway in such an exciting field is to remain curious, constantly refine your skills, and stay updated with new research and advancements.
Claus Aranha;Christian Leonardo Camacho-Villalón;Felipe Campelo;Marco Dorigo
(2021)Antoine Ligot;Mauro Birattari
(2020)Chanelle Lee;Chanelle Lee;Jonathan Lawry;Alan F. T. Winfield
(2021)Daniel H. Stolfi;Matthias R. Brust;Grégoire Danoy;Pascal Bouvry
(2021)Nicolas Coucke;Mary Katherine Heinrich;Axel Cleeremans;Marco Dorigo
(2021)Karina A. Roundtree;Jason R. Cody;Jennifer Leaf;H. Onan Demirel
(2021)Pursuing a Computer Science degree online offers flexible options for students balancing work and study. Many choose online self paced colleges to progress at their own speed, which is ideal for mastering complex topics without the pressure of fixed schedules.
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