Environmental Engineering Reference
In-Depth Information
10.1 Introduction to Swarms
In nature, swarms are large groupings of insects such as bees or locusts where
each insect has a simple role, but where the swarm as a whole produces com-
plex behaviors. Strictly speaking, such emergence of complex behavior is not
limited to swarms, and there are similar complex social structures occurring
with higher order animals and insects that do not swarm per se such as colonies
of ants, flocks of birds, packs of wolves, etc. The idea that swarms can be used
to solve complex problems has been taken up in several areas of computer sci-
ence. The term “swarm” in this topic refers to a large grouping of simple
components working together to achieve some goal and produce significant
results [ 12 ]. The result of combining simple behaviors (the microscopic be-
havior) is the emergence of complex behavior (the macroscopic behavior) and
the ability to achieve significant results as a “team” [ 16 ]. The term should not
be taken to imply that these components fly (or are airborne); they may just
as well operate on the surface of the earth, under the surface, under water, or
in space (including other planets).
Intelligent swarm technology is based on swarm technology where the in-
dividual members of the swarm also exhibit independent intelligence [ 13 ], and
thus, act as agents. Intelligent swarms may be heterogeneous or homogeneous.
Even if the swarm starts out as homogeneous, the individual members, with
differing environments, may learn different things and develop different goals,
and in this way, the swarm becomes heterogeneous. Intelligent swarms may
also be made up of heterogeneous elements from the outset, reflecting different
capabilities as well as a possible social structure.
Agent swarms are being used in computer modeling and have been used
as a tool to study complex systems [ 55 ]. Examples of simulations that have
been undertaken include swarms of birds [ 21 , 115 ], problems in business and
economics [ 93 ], and ecological systems [ 131 ]. In swarm simulations ,eachof
the agents is given certain parameters that it tries to maximize. In terms of
bird swarms, each bird tries to find another bird to fly with, and then flies
off to one side and slightly higher to reduce its drag, and eventually the birds
form flocks. Other types of swarm simulations have been developed that ex-
hibit unlikely emergent behavior. These emergent behaviors are the sums of
often simple individual behaviors, but, when aggregated, form complex and
often unexpected behaviors. Swarm behavior is also being investigated for use
in such applications as telephone switching, network routing, data categoriza-
tion, command and control systems, and shortest path optimizations.
Swarm intelligence techniques (note the slight difference in terminology
from “intelligent swarms”) are population-based stochastic methods used in
combinatorial optimization problems. In these models, the collective behav-
ior of relatively simple individuals arises from local interactions between each
individual and its environment and between each individual and other mem-
bers of the swarm, which finally results in the emergence of global functional
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