Editorial Swarm Intelligence and Its Applications (original) (raw)

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.

Overview of Algorithms for Swarm Intelligence

Computational Collective Intelligence. Technologies and Applications, 2011

Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.

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.

Introductory Review of Swarm Intelligence Techniques

Studies in computational intelligence, 2022

With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.

Swarm Intelligence and Its Applications 2014

Swarm intelligence (SI) represents the collective behavior of decentralized, self-organized systems. SI systems consist typically of a population of simple agents that interact locally with one another and with their environment. The inspiration of SI originates from 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 intelligence, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. Besides the applications to conventional optimization problems, SI is employed in various fields such as library materials acquisition, communications, medical dataset classification, dynamic control, heating system planning, moving objects tracking, pattern recognition, and statistical prediction.

Swarm intelligence: foundations, perspectives and applications

2006

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged [14, 7, 22].

Swarm Intelligence an Inspiration from Social Insect Behavior in Various Decision Making Algorithms

A swarm is a large number of homologous, intelligible workers collaborating locally among themselves, and their environment, with no paramount control. The intelligence possessed by these swarms is known as swarm intelligence. Swarm Intelligence (SI) is a branch of Artificial Intelligence (AI) that is used to model the cumulative behavior of social swarms in nature, such as ant colonies, honey bees, and bird flocks. This paper focuses on the how the intelligence from the swarms has exhilarated for the development of various algorithms for solving hard problems and it also outlines two of the most acknowledged methods of optimization techniques motivated by swarm intelligence: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

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