Swarm Intelligence

1999 ◽  
Author(s):  
Eric Bonabeau ◽  
Marco Dorigo ◽  
Guy Theraulaz
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Social Insects ◽  
Operations Research ◽  
Social Insect ◽  
Multiagent System ◽  
Collective Intelligence ◽  
Insect Behavior ◽  
Changing Environments ◽  
Interacting Agents

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

Swarm-Based Techniques in E-Learning

2011 ◽  
pp. 199-212
Author(s):  
Sergio Gutiérrez ◽  
Abelardo Pardo
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Social Insects ◽  
Learning System ◽  
The Internet ◽  
Artificial Intelligence Technique ◽  
E Learning ◽  
Weak Points ◽  
Intelligence Technique

This chapter provides an overview of the use of swarm-intelligence techniques in the field of e-learning. Swarm intelligence is an artificial intelligence technique inspired by the behavior of social insects. Taking into account that the Internet connects a high number of users with a negligible delay, some of those techniques can be combined with sociology concepts and applied to e-learning. The chapter analyzes several of such applications and exposes their strong and weak points. The authors hope that understanding the concepts used in the applications described in the chapter will not only inform researchers about an emerging trend, but also provide with interesting ideas that can be applied and combined with any e-learning system.

scholarly journals Embedding Swarm Intelligence in Internet of Things Networks: A Social Insect Approach

2020 ◽  
pp. 80-87
Author(s):  
Sergio V Davalos, Ph.D.
Keyword(s):  
Internet Of Things ◽  
Swarm Intelligence ◽  
Social Insect ◽  
Insect Behavior ◽  
Agent Based Modeling ◽  
The Internet ◽  
Evolutionary Adaptation ◽  
Agent Based ◽  
The Internet Of Things

This paper introduces a framework for embedding intelligence in the Internet of Things (IoT) networks. The framework draws upon agent-based modeling, swarm intelligence, social insect behavior, and evolutionary adaptation. The key principles for each of these areas are first discussed. These concepts are then discussed from an IoTs perspective. The resulting capabilities and potential of embedding this type of intelligence are outlined.

Swarm Intelligence: A Primer

2018 ◽  
Vol 8 (5) ◽  
pp. 100
Author(s):  
Matthew N. O. Sadiku ◽  
Mahamadou Tembely ◽  
Sarhan M. Musa
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Soft Computing ◽  
Collective Intelligence ◽  
Field Of Study ◽  
Biological Entities

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.

scholarly journals Transcriptomic analyses of the termite, Cryptotermes secundus, reveal a gene network underlying a long lifespan and high fecundity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Silu Lin ◽  
Jana Werle ◽  
Judith Korb
Keyword(s):  
Life History ◽  
Social Insects ◽  
Social Communication ◽  
Gene Network ◽  
Social Insect ◽  
Nutrient Sensing ◽  
Model Organisms ◽  
Termite Species ◽  
High Fecundity ◽  
Trade Off

AbstractOrganisms are typically characterized by a trade-off between fecundity and longevity. Notable exceptions are social insects. In insect colonies, the reproducing caste (queens) outlive their non-reproducing nestmate workers by orders of magnitude and realize fecundities and lifespans unparalleled among insects. How this is achieved is not understood. Here, we identified a single module of co-expressed genes that characterized queens in the termite species Cryptotermes secundus. It encompassed genes from all essential pathways known to be involved in life-history regulation in solitary model organisms. By manipulating its endocrine component, we tested the recent hypothesis that re-wiring along the nutrient-sensing/endocrine/fecundity axis can account for the reversal of the fecundity/longevity trade-off in social insect queens. Our data from termites do not support this hypothesis. However, they revealed striking links to social communication that offer new avenues to understand the re-modelling of the fecundity/longevity trade-off in social insects.

scholarly journals Oral transfer of chemical cues, growth proteins and hormones in social insects

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Adria C LeBoeuf ◽  
Patrice Waridel ◽  
Colin S Brent ◽  
Andre N Gonçalves ◽  
Laure Menin ◽  
...  
Keyword(s):  
Social Insects ◽  
Social Insect ◽  
Oral Fluid ◽  
Chemical Cues ◽  
Fluid Exchange ◽  
Camponotus Floridanus ◽  
Growth Development ◽  
Behavioral Maturation ◽  
Related Proteins ◽  
Complete Metamorphosis

Social insects frequently engage in oral fluid exchange – trophallaxis – between adults, and between adults and larvae. Although trophallaxis is widely considered a food-sharing mechanism, we hypothesized that endogenous components of this fluid might underlie a novel means of chemical communication between colony members. Through protein and small-molecule mass spectrometry and RNA sequencing, we found that trophallactic fluid in the ant Camponotus floridanus contains a set of specific digestion- and non-digestion related proteins, as well as hydrocarbons, microRNAs, and a key developmental regulator, juvenile hormone. When C. floridanus workers’ food was supplemented with this hormone, the larvae they reared via trophallaxis were twice as likely to complete metamorphosis and became larger workers. Comparison of trophallactic fluid proteins across social insect species revealed that many are regulators of growth, development and behavioral maturation. These results suggest that trophallaxis plays previously unsuspected roles in communication and enables communal control of colony phenotypes.

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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