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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
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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
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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
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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
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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,
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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
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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
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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
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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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