Advances in Swarm and Computational Intelligence (original) (raw)

Editorial: Special Issue on Swarm Intelligence Algorithms and Applications

2011

Swarm intelligence (SI) is generally to study the collective behaviour in a decentralized system which is made up by a population of simple individuals interacting locally with one another and with their environment. Such systems are often be found in nature, including bird flocking, ant colonies, particles in cloud, fish schooling, bacteria foraging, animal herding, honey bees, spiders, and sharks, just to name a few.

Swarm Intelligence - Recent Advances, New Perspectives and Applications [Working Title]

Swarm Intelligence - Recent Advances, New Perspectives and Applications, 2019

received his first PhD in Telecommunication Engineering from the University of Navarra, Spain, and his second PhD in Computational Intelligence from the University of Alcala, Spain. Currently he is a Research Professor in Artificial Intelligence and the leading scientist of the OPTIMA (Optimization, Modeling and Analytics) research area at TECNALIA RESEARCH & INNOVATION (www.tecnalia.com). He is also an adjunct professor at the University of the Basque Country (UPV/EHU), an invited research fellow at the Basque Center for Applied Mathematics (BCAM), and a senior AI advisor at the technological startup SHERPA.AI. He is also the coordinator of a Joint Research Lab. His research interests include the use of Artificial Intelligence methods for data mining and optimization. He has published more than 280 scientific articles, co-supervised 8 PhD theses (+ 9 ongoing), edited 6 books, co-authored 9 patents, and participated/led more than 40 research projects. He has also been involved in the organization of various national and international conferences. He is a senior member of the IEEE, and a recipient of the Bizkaia Talent prize for his research career. Dr. Esther Villar holds a PhD in Information and Communication Technologies (2015) from the University of Alcalá (Spain). She achieved her Computer Scientist degree (2010) from the University of Deusto, and her MSc (2012) in Computer Languages and Systems from the UNED (National University of Distance Education). Her areas of interest and knowledge include Natural Language Processing (NLP), detection of impersonation in social networks, semantic web and machine learning. She has made several contributions at conferences and has published in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of Tecnalia's ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes: resource planning, energy efficiency, etc. Dr. Eneko Osaba works at TECNALIA as a researcher in the ICT/ OPTIMA area. He obtained his Ph.D. degree on Artificial Intelligence in 2015 from the University of Deusto. He has participated in the proposal, development, and justification of more than 20 local and European research projects, and in the publication of more than 100 scientific papers (including more than 20 Q1). He has performed several fellowships at universities in the United Kingdom, Italy, and Malta. Eneko has served as the program committee member in more than 30 international conferences and participated in organizing activities in more than 7 international conferences. He is a member of the editorial board of

Swarm Intelligence and Its Applications

The Scientific World Journal, 2013

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Swarm Intelligence: A Primer

International Journal of Advanced Research in Computer Science and Software Engineering

Swarm intelligence is the emergent collective intelligence of groups of simple agents. It belongs to the emerging field of bio-inspired soft computing. It is inspired from the biological entities such as birds, fish, ants, wasps, termites, and bees. Bio-inspired computation is a field of study that is closely related to artificial intelligence. This paper provides a brief introduction to swarm intelligence.

Editorial Swarm Intelligence and Its Applications

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Swarm Intelligence: Past, Present and Future

Soft Computing, 2017

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.