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What is Swarm Intelligence (SI)

Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
AI field dedicated to the study of how social organisms perform an intelligent task through local individual interactions.
Published in Chapter:
Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
André S. Fialho (Massachusetts Institute of Technology, USA), Federico Cismondi (Massachusetts Institute of Technology, USA), Susana M. Vieira (Technical University of Lisbon, Portugal), Shane R. Reti (Harvard University, USA), João M. C. Sousa (Technical University of Lisbon, Portugal), and Stan N. Finkelstein (Massachusetts Institute of Technology, USA)
DOI: 10.4018/978-1-4666-3990-4.ch016
Abstract
During the last decade, modern hospitals have witnessed a growth in the amount of information acquired, stored, and retrieved more than ever before. While aimed at helping healthcare personnel in providing care to patients, this high stream of data can also have a negative impact if not delivered in a simple and organized way. In this chapter, the authors explore the current opportunities and challenges that soft computing predictive tools face in healthcare delivery, and they then present an example of how some of these tools may contribute to the decision-making of health care providers for an important critical condition in Intensive Care Units (ICU)—septic shock. Despite current challenges, such as the availability of clean clinical data, accuracy, and interpretability, these systems will likely act to enhance the performance of a human expert and permit healthcare resources to be used more efficiently while maintaining or improving outcomes.
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Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
AI field dedicated to the study of how social organisms perform an intelligent task through local individual interactions.
Full Text Chapter Download: US $37.50 Add to Cart
Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments
Is a discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, SI focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals.
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Applications of Swarm Intelligence in Remanufacturing
Built on the core principles found in various natural systems which is composed of many agents who exploits local communication forms and highly distributed control. Informally, SI is a kind of computational metaphor inspired by different swarm examples such as ants, wasps, honey bees, fish, birds, sheep, wolves, and particles.
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Modified Multi-Grey Wolf Pack for Vital Sign-Based Disease Identification
It is basically population-based population. It depends on natural behavior of animals, bird etc. It utilizes the concept that a number of particles (candidate solution) which fly around the search space to find the best solution.
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Adaptive Algorithms for Intelligent Geometric Computing
The property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge.
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The Bees Algorithm as a Biologically Inspired Optimisation Method
Collective intelligence of societies of biological (social animals) or artificial (robots, computer agents) individuals. In artificial intelligence, it gave rise to a computational paradigm based on decentralisation, self-organisation, local interactions, and collective emergent behaviours.
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Intelligent Information Systems
Refers to a class of algorithms inspired by the collective behaviour of insect swarms, ant colonies, the flocking behaviour of some bird species, or the herding behaviour of some mammals, such that the behaviour of the whole can be considered as exhibiting a rudimentary form of “intelligence.”
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TAntNet-4: A Threshold-Based AntNet Algorithm with Improved Scout Behavior
Swarm intelligence appears in nature with different swarms, like Ants, Bees, flock of birds, and school of fish...etc. swarm intelligence were a source of inspiration for many self-organized systems that used to solve complex problems. Swarm intelligence systems composed of many unsophisticated agents that coordinate using decentralized control.
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Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification
It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure.
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Applying CI in Biology through PSO
SI is one of the techniques which is inspired by nature and is a population based algorithm motivated by the collective behaviour of a group of social insects.
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