Adaptive Collective Systems - Herding black sheepFoCAS Initiative
This book is about understanding, designing, controlling, and governing adaptive collective systems. It is intended for readers from master's students to Ph.D. students, from engineers to decision makers, and anyone else who is interested in understanding how technologies are changing the way we think and live.
The authors are academics working in various areas of a new rising field: adaptive collective systems.
Stuart Anderson (The University of Edinburgh, United Kingdom)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
A.E. Eiben (VU University Amsterdam, Netherlands)
George Kampis (DFKI, Germany)
Maarten van Steen (VU University Amsterdam, Netherlands)
Book Sprint collaborative writing session facilitator: Adam Hyde
Editor: Sandra Sarala
Designer: Henrik van Leeuwen
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO)
based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO
has been inspired from natural ants system, their behavior, team coordination, synchronization for the
searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as
a leading metaheuristic technique for the solution of combinatorial optimization problems which can be
used to find shortest path through construction graph. This paper describe about various behavior of ants,
successfully used ACO algorithms, applications and current trends. In recent years, some researchers
have also focused on the application of ACO algorithms to design of wireless communication network,
bioinformatics problem, dynamic problem and multi-objective problem.
Swarm intelligence systems often comprise a population of essential agents interacting locally with one another and their surroundings. Again, nature, particularly biological systems, is a frequent source of inspiration. Although no centralized control structure dictates how individual agents should behave, local and, to some extent, random interactions between such agents create "intelligent" global behaviour unknown to the respective agents.
Adaptive Collective Systems - Herding black sheepFoCAS Initiative
This book is about understanding, designing, controlling, and governing adaptive collective systems. It is intended for readers from master's students to Ph.D. students, from engineers to decision makers, and anyone else who is interested in understanding how technologies are changing the way we think and live.
The authors are academics working in various areas of a new rising field: adaptive collective systems.
Stuart Anderson (The University of Edinburgh, United Kingdom)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
A.E. Eiben (VU University Amsterdam, Netherlands)
George Kampis (DFKI, Germany)
Maarten van Steen (VU University Amsterdam, Netherlands)
Book Sprint collaborative writing session facilitator: Adam Hyde
Editor: Sandra Sarala
Designer: Henrik van Leeuwen
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO)
based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO
has been inspired from natural ants system, their behavior, team coordination, synchronization for the
searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as
a leading metaheuristic technique for the solution of combinatorial optimization problems which can be
used to find shortest path through construction graph. This paper describe about various behavior of ants,
successfully used ACO algorithms, applications and current trends. In recent years, some researchers
have also focused on the application of ACO algorithms to design of wireless communication network,
bioinformatics problem, dynamic problem and multi-objective problem.
Swarm intelligence systems often comprise a population of essential agents interacting locally with one another and their surroundings. Again, nature, particularly biological systems, is a frequent source of inspiration. Although no centralized control structure dictates how individual agents should behave, local and, to some extent, random interactions between such agents create "intelligent" global behaviour unknown to the respective agents.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science.
Emergent Behavior and SCM Introduction In this exercise, the .docxSALU18
Emergent Behavior and SCM
Introduction:
In this exercise, the student will analyze emergent behavior as it applies to SCM.
Tasks:
Read "Executive Insight in Hugos": Essentials of Supply Chain Management, answer the following questions:
• Explain how negative feedback improves the performance of a supply chain.
• Describe the steps that managers can take to encourage positive emergent behavior in their supply chains.
• Why is emergent behavior important to continued success?
2-3 pages. APA citations.
Emergent behavior is what happens when an interconnected system of relatively simple elements begins to self-organize to form a more intelligent and more adaptive higher-level system. Steven Johnson in his book, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, explores the conditions that bring about this phenomenon.
In an interview with Steven Johnson I posed six questions and asked him to share his insights on a range of topics. These topics range from what gives a system emergent characteristics to how could companies organize their supply chains so as to encourage and benefit from emergent behavior.
· What is an “emergent system”? How is an emergent system different from an assembly line? The catchphrase that I sometimes use is that an emergent system is “smarter” than the sum of its parts. They tend to be systems made up of many interacting agents, each of which is following relatively simple rules governing its encounters with other agents. Somehow, out of all these local interactions, a higher-level, global intelligence “emerges.” The extraordinary thing about these systems is that there's no master planner or executive branch—the overall group creates the intelligence and adaptability; it's not something passed down from the leadership. An ant colony is a great example of this: colonies manage to pull off extraordinary feats of resource management and engineering and task allocation, all by following remarkably simple rules of interaction, using a simple chemical language to communicate. There's a queen ant in the colony, but she's only called that because she's the chief reproductive engine for the colony—she doesn't have any actually command authority. The ordinary ants just do the thinking collectively, without a leader. A key difference between an emergent system and an assembly line lies in the fluidity of the emergent system: randomness is a key component of the way an ant colony will explore a given environment—take the random element out, and the colony gets much less interesting, much less capable of stumbling across new ideas. Assembly lines are all about setting fixed patterns, and eliminating randomness; emergence is all about stumbling across new patterns that work better than the old ones.
· You say that such systems are “bottom up systems, not top-down.” These systems solve problems by drawing on masses of simple elements instead of relying on a single, intelligent “executive branch.” What ...
Bio-inspired Artificial Intelligence for Collective SystemsAchini_Adikari
Artificial Intelligence is a constantly growing field of study. Today, there is an emerging interest to bind concepts natural systems to computing to develop self-organized machines
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
Optimizing community detection in social networks using antlion and K-medianjournalBEEI
Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.
Analysis of Machine Learning Techniques for Breast Cancer PredictionDr. Amarjeet Singh
The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science.
Emergent Behavior and SCM Introduction In this exercise, the .docxSALU18
Emergent Behavior and SCM
Introduction:
In this exercise, the student will analyze emergent behavior as it applies to SCM.
Tasks:
Read "Executive Insight in Hugos": Essentials of Supply Chain Management, answer the following questions:
• Explain how negative feedback improves the performance of a supply chain.
• Describe the steps that managers can take to encourage positive emergent behavior in their supply chains.
• Why is emergent behavior important to continued success?
2-3 pages. APA citations.
Emergent behavior is what happens when an interconnected system of relatively simple elements begins to self-organize to form a more intelligent and more adaptive higher-level system. Steven Johnson in his book, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, explores the conditions that bring about this phenomenon.
In an interview with Steven Johnson I posed six questions and asked him to share his insights on a range of topics. These topics range from what gives a system emergent characteristics to how could companies organize their supply chains so as to encourage and benefit from emergent behavior.
· What is an “emergent system”? How is an emergent system different from an assembly line? The catchphrase that I sometimes use is that an emergent system is “smarter” than the sum of its parts. They tend to be systems made up of many interacting agents, each of which is following relatively simple rules governing its encounters with other agents. Somehow, out of all these local interactions, a higher-level, global intelligence “emerges.” The extraordinary thing about these systems is that there's no master planner or executive branch—the overall group creates the intelligence and adaptability; it's not something passed down from the leadership. An ant colony is a great example of this: colonies manage to pull off extraordinary feats of resource management and engineering and task allocation, all by following remarkably simple rules of interaction, using a simple chemical language to communicate. There's a queen ant in the colony, but she's only called that because she's the chief reproductive engine for the colony—she doesn't have any actually command authority. The ordinary ants just do the thinking collectively, without a leader. A key difference between an emergent system and an assembly line lies in the fluidity of the emergent system: randomness is a key component of the way an ant colony will explore a given environment—take the random element out, and the colony gets much less interesting, much less capable of stumbling across new ideas. Assembly lines are all about setting fixed patterns, and eliminating randomness; emergence is all about stumbling across new patterns that work better than the old ones.
· You say that such systems are “bottom up systems, not top-down.” These systems solve problems by drawing on masses of simple elements instead of relying on a single, intelligent “executive branch.” What ...
Bio-inspired Artificial Intelligence for Collective SystemsAchini_Adikari
Artificial Intelligence is a constantly growing field of study. Today, there is an emerging interest to bind concepts natural systems to computing to develop self-organized machines
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
Optimizing community detection in social networks using antlion and K-medianjournalBEEI
Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.
Analysis of Machine Learning Techniques for Breast Cancer PredictionDr. Amarjeet Singh
The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support vector machine (SVM), K-closest neighbours, irregular woodlands, fake/ Artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
1. Advantages And Disadvantages Of Bee Colony
ABSTRACT
Artificial Intelligence has many domains Swarm Intelligence being one of them.Swarm Intelligence
deals with study of actions of individuals in various decentralised systems.The Bee Colony
Optimisation (BCO) metaheuristic has been introduced real recently as a new division of Swarm
Intelligence.Artificial bees in the algorithms implemented represent agents which collaboratively
solve optimization problems.It is a rising field for researchers in the field of optimization problems
because it provides tremendous problem solving range for combinatorial and NP–hard
problems.BCO is one of the benchmark systems displaying team work and cooperative work. BCO
is a bottom–up approach of modeling where agents form global solution by optimizing ... Show
more content on Helpwriting.net ...
Few of them are
* Particle Swarm Optimisation
* Ant Colony Optimisation
* Bee Colony Optimisation
* Bee swarm Optimisation
* Artificial Bee Colony
*Hybrid Bee Colony Optimisation
There are several advantages:
A. The agents react to a action rather than plan it,hence they are not goal directed .
B. Agents are simplistic in nature , having very less memory and minimal behaviour.
C. With no global information in the system the command is decentralized .
D.As emergent behavious adapts to changes with respect to a single entity failure , one agent failure
is thus permitted.
E. Dynamic changes in surrounding affects the agents
F There is very little or no direct interaction among agents.
There are certain disadvantages:
2. A.The study of the actions or characteristiccs of a single agent wont help to gather information
about the whole swarm.Hence choosing swarm defeating behaviour will be difficult.
B.Single agents behaviour is random and noisy as action course is stochastic.
C.Due to lack of analytical mechanism ,creating the design for swarm based system is difficult.
D. The parameters given or provided as input can result in drastic effect on the outgrowth of
collective
... Get more on HelpWriting.net ...
3.
4. Swarm Intelligence: Concepts, Models, and Applications
Swarm Intelligence: Concepts, Models and Applications
Technical Report 2012–585
Hazem Ahmed Janice Glasgow
School of Computing Queen 's University Kingston, Ontario, Canada K7L3N6
{hazem, janice}@cs.queensu.ca
February 2012
Report Index
1. 2.
Introduction ........................................................................................................................ 2
Swarm Intelligence (SI) Models ......................................................................................... 4
2.1 Ant Colony Optimization (ACO) Model ........................................................................ 4 2.1.1
Ants in ... Show more content on Helpwriting.net ...
39
1
1.
Introduction
A swarm is a large number of homogenous, simple agents interacting locally among themselves, and
their environment, with no central control to allow a global interesting behaviour to emerge.
Swarm–based algorithms have recently emerged as a family of nature–inspired, population–based
algorithms that are capable of producing low cost, fast, and robust solutions to several complex
problems 1]2]. Swarm Intelligence [ [ (SI) can therefore be defined as a relatively new branch of
Artificial Intelligence that is used to model the collective behaviour of social swarms in nature, such
as ant colonies, honey bees, and bird flocks. Although these agents (insects or swarm individuals)
are relatively unsophisticated with limited capabilities on their own, they are interacting together
with certain behavioural patterns to cooperatively achieve tasks necessary for their survival. The
social interactions among swarm individuals can be either direct or indirect 3]. Examples of direct
interaction are through visual or audio contact, such as [ the waggle dance of honey bees. Indirect
5. interaction occurs when one individual changes the environment and the other individuals respond
to the new environment, such as the pheromone trails of ants that they deposit on their way to search
for food sources. This indirect type of
... Get more on HelpWriting.net ...
6.
7. Collective Behavior And Stigmergy Of Cancer Cells
University of Warwick
Erasmus Mundus MSc in Complex Systems Science
M1 project report
Collective behaviour and stigmergy in populations of cancer cells
Author:
Supervisors:
Jacopo Credi
Prof. Jean–Baptiste Cazier
Dr. Sabine Hauert
Dr. Anne Straube
June 18, 2015
Abstract
Investigating and capturing the emergence of collective phenomena in cancer cell migration can
advance our understanding of the process of tissue invasion, which is one of the first steps leading to
the formation of metastases, or secondary tumours. By reconstructing the trajectories of lung cancer
cells populations from microscopy image sequences, we were able to analyse their collective two–
dimensional dynamics and measure the system spatial correlation function in different density
conditions. This revealed that cancer cells, similarly to other recently studied biological systems, can
exhibit a form of collective dynamics without global order. However, the observed density
dependence of the correlation function differed completely from the theoretical predictions of
standard models of moving particles with mechanisms of local alignment. We propose an
explanation for this unexpected finding, supported by an analysis of the role of density in the ability
of cells to communicate through the micro–environment (stigmergy), which revealed the emergence
of a network–like structure of trails when the system density was sufficiently low.
1
Introduction modelling, driven by the massive amount of data produced in
... Get more on HelpWriting.net ...
8.
9. Swarm Intelligence : An Organization
Swarm intelligence:
Nature presents suggestion to the humans in many ways. One way of such inspiration is the best
way in which ordinary organisms behave when they 're in groups. example a swarm of ants, a
swarm of bees, a colony of microorganism, in these scenario and in many other, biologists have
informed us that the workforce of group of individuals itself reveals behavior that the character
individuals don 't, or cannot. In other phrases, if we recall the workforce itself as an individual or the
swarm in some ways, at least, the whole swarm seems to be more intelligent than any of the
members inside it. This remark is the seed for a mass of principles and algorithms, a few of which
have become related to swarm intelligence. It turns out that swarm intelligence is handiest closely
related to a small element of this mass of principles and algorithms. If we search nature for scenarios
wherein a group of agents reveals behavior that the individual doesn't, it is effortless to find entire
and enormous sub–areas of science, certainly in the bio–sciences. Any biological organism seems to
exemplify this thought, once we keep in mind the character organism because the 'swarm ', and its
cellular add–ons as agents. We could consider brains, and worried programs regularly, as a supreme
exemplar of this idea, when person neurons are regarded because the agents or we might zoom in on
precise inhomogeneous units of bio–molecules as our 'sellers ', and herald gene transcription, say,
... Get more on HelpWriting.net ...
10.
11. Case Study Of Ant Colony Optimization
1.6 Ant Colony Optimization: 1.6.1 Introduction The conception of massive Gordian behavior
emerging from the behavior of many relatively simple units, and the dealings with them, is
fundamental to the field of Artificial Intelligence. The growing understanding of such systems offers
the prospect of creating artificial systems which are controlled by such significant shared behavior;
in particular, we believe that the sweating of this concept might lead to entirely new channels for the
pilotage of distributed systems, such as load balancing in telecommunications networks. In such
regimes, requests between any two points typically route through some central switching bureau or
nodes in a big system; there are many future paths for each such call. It is thus viable to emancipate
actual or potential local hurdles by routing requests via parts of the system which have extra
capacity. Load balancing is essentially the creation of call–routing schemes which successfully
assign the changing load over the system and curtail lost requests. Of course, it is viable to
determine the shortest routes from every node to every other node of the network. With the help of
this mean utilization of nodes reduces, but this is not necessarily the drift way to avoid node
congestion, as this has to do with how the vivisection of the traffic on the network. Controlling
distributed systems through a single central controller has several repugnancies. The ... Show more
content on Helpwriting.net ...
A type of sign–based stigmergy is used in our network model. It is based on the way ants discover
the shortest path from their nest to a food source and also on the way they select between food
sources of different value. The way ants organize these routes has inspired us to investigate a new
attain for the avoidance of congestion in telecommunications
... Get more on HelpWriting.net ...
12.
13. Advantages Of Ant Colony Optimization
Abstract Ant colony optimization is a technique for solving problem which are hard in nature. These
kinds of problems need only the optimal solution. ACO provide a solution based on the behaviour of
ants searching for food. This paper list out various techniques used to solve such problems and their
advantages, disadvantages. Through this review we identify some suggestion for solving NP hard
problem.
Keywords :Ant colony optimization, NP hard, stigmergy, Pheromone, optimal , heuristics
1. Introduction
1.1 Swarm Intelligence
Swarm intelligence was been proposed and induced in the Artificial Intelligence by Beni and Wang
[10] in 1989. It was been applied in the context of cellular robotic systems. Swarm Intelligence are
basically small agents or living organisms which will collectively work together and make
optimality or the group work will end with some beneficiary report. Inspiration from those group
works and turning it into the artificial things and making those ideas possible in the computers to
attain some sort of goal is called swarm intelligence. The inspiration basically from the nature and to
be ... Show more content on Helpwriting.net ...
This article presents various kind of situations and problems that the ACO can be applied to find out
optimal solutions where the problem consists of many constraints. Fuzzy problems and uncertainty
problems can also be optimized by ACO. For example Traffic Signalling Problem: in this the traffic
signal will be safe until or unless the capacity of the signal increase and once it occurs there may be
a chance of colliding somewhere either in the front or in the tail of the signal. It may result in a loss
of huge timing may be also in days and days of time. ACO can be able to avoid these kinds of
problems and provide a clear idea on how to handle those situations with some kind of simulation
with the help of
... Get more on HelpWriting.net ...
14.
15. Advantages Of Swarm Robotics
Advances in Swarm Robotics and the Stability of a Swarm
INTRODUCTION
Swarm robotics is the new approach to the coordination of multi–robot systems that consists of
many relatively smaller robots. The inspiration of this is the social behaviour of many animals and
insects like ants, geese etc. the terms "Swarm Intelligence" refers to the collective behaviour that is
the outcome of the work of the smaller individuals, each acting autonomously. Swarm intelligence is
a property of systems of non–intelligent exhibiting a collectively intelligent behaviour.
Since the 1980's, swarm robotics has become a major area of research. As new solution approaches
are being developed and validated, it is often possible to realize the advantages of swarm robotic
systems.
In a paper by Dudek et al in 1993, the research on swarm robotics was classified into five areas
which are swarm size, communication range, communication topology, communication bandwidth,
swarm re–configurability and swarm unit processing ability. In another paper by Cao et al, the
survey of cooperative robotics was presented in a hierarchical way. This publication was also split
onto five main parts: group architecture, resource conflicts, origins of cooperation, learning and
geometric problems.
In a paper by Hiroshi et al the stability of a swarm ... Show more content on Helpwriting.net ...
Social insects have the best examples of the best self–organized behaviour. For example, ants and
bees are not as powerful alone as they are with their colony or hive respectively. By means of local
and limited communication, they are able to accomplish impressive behavioural feats: maintaining
the health of the colony, caring for their young, responding to invasion and so on. Ants leave a
chemical substance, pheromone, behind to help guide other ants at a later point in time. Thomas et al
were inspired from ants and they made a swarm which would search, retrieve, return, deposit and
rest an
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