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Swarm Intell (2007) 1: 3–31
DOI 10.1007/s11721-007-0004-y
The biological principles of swarm intelligence
Simon Garnier ·Jacques Gautrais ·Guy Theraulaz
Received: 6 February 2007 / Accepted: 21 May 2007 / Published online: 17 July 2007
© Springer Science + Business Media, LLC 2007
Abstract The roots of swarm intelligence are deeply embedded in the biological study of
self-organized behaviors in social insects. From the routing of traffic in telecommunication
networks to the design of control algorithms for groups of autonomous robots, the collective
behaviors of these animals have inspired many of the foundational works in this emerging
research field. For the first issue of this journal dedicated to swarm intelligence, we review
the main biological principles that underlie the organization of insects’ colonies. We begin
with some reminders about the decentralized nature of such systems and we describe the un-
derlying mechanisms of complex collective behaviors of social insects, from the concept of
stigmergy to the theory of self-organization in biological systems. We emphasize in partic-
ular the role of interactions and the importance of bifurcations that appear in the collective
output of the colony when some of the system’s parameters change. We then propose to
categorize the collective behaviors displayed by insect colonies according to four functions
that emerge at the level of the colony and that organize its global behavior. Finally, we ad-
dress the role of modulations of individual behaviors by disturbances (either environmental
or internal to the colony) in the overall flexibility of insect colonies. We conclude that fu-
ture studies about self-organized biological behaviors should investigate such modulations
to better understand how insect colonies adapt to uncertain worlds.
Keywords Swarm intelligence ·Social insects ·Stigmergy ·Self-organization collective
behaviors
1 Introduction
Swarm intelligence, as a scientific discipline including research fields such as swarm opti-
mization or distributed control in collective robotics, was born from biological insights about
the incredible abilities of social insects to solve their everyday-life problems (Bonabeau
S. Garnier ()·J. Gautrais ·G. Theraulaz
Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Université Paul Sabatier, Bât 4R3,
118 Route de Narbonne, 31062 Toulouse cedex 9, France
e-mail: simon.garnier@cict.fr
4 Swarm Intell (2007) 1: 3–31
et al. 1999). Their colonies ranging from a few animals to millions of individuals, dis-
play fascinating behaviors that combine efficiency with both flexibility and robustness
(Camazine et al. 2001). From the traffic management on a foraging network (Burd 2006;
Couzin and Franks 2003; Dussutour et al. 2004; Vittori et al. 2006), to the building of effi-
cient structures (Buhl et al. 2004,2005; Theraulaz et al. 2003; Tschinkel, 2003,2004), along
with the dynamic task allocation between workers (Beshers and Fewell 2001; Bonabeau
et al. 1998; Deneubourg et al. 1987; Gordon 1996), examples of complex and sophisti-
cated behaviors are numerous and diverse among social insects (Bonabeau et al. 1997;
Camazine et al. 2001; Detrain and Deneubourg 2006).
For example, in their moving phase, the neotropical army ants Eciton burchelli may orga-
nize large hunting raids which may contain more than 200 000 workers collecting thousands
of prey, be 15 m or more wide and sweep over an area of more than 1500 m2in a single day
(Franks 1989; Franks and Fletcher 1983; Hölldobler and Wilson 1990). As another example,
African termites of the species Macrotermes bellicosus build mounds that may reach a di-
ameter of 30 m and a height of 6 m (Grassé 1984). These biological skyscrapers result from
the work of millions of tiny (1–2 mm long) and completely blind individuals. Even more
fascinating than the size of these mounds is their internal structure. Nests of the species Api-
cotermes lamani are probably one of the most complex structures ever built in the animal
kingdom (Desneux 1956,seeFig.1(a)). Over the outside surface of the nest, there exists a
whole set of micro structures that ensure air conditioning and gas exchanges with the outside
environment. Inside these nests, that are about 20 to 40 centimeters high, we find a succes-
sion of chambers connected together with helical ramps. These helical ramps arise from the
twisting and soldering of successive floors. There are several stairs at each floor and some
of these stairs go through the whole nest. Even distant chambers are in connection through
these shortcuts.
Surprisingly, the complexity of these collective behaviors and structures does not re-
flect at all the relative simplicity of the individual behaviors of an insect. Of course, in-
sects are elaborated “machines”, with the ability to modulate their behavior on the basis of
the processing of many sensory inputs (Menzel and Giurfa 2001; Detrain and Deneubourg
2006). Nevertheless, as pointed out by Seeley (2002), the complexity of an individual insect
in terms of cognitive or communicational abilities may be high in an absolute sense, while
remaining not sufficient to effectively supervise a large system and to explain the complexity
of all the behaviors at the colony scale. In most cases, a single insect is not able to find by
itself an efficient solution to a colony problem, while the society to which it belongs finds
“as a whole” a solution very easily (Camazine et al. 2001).
Behind this “organization without an organizer” are several hidden mechanisms which
enable insect societies, whose members only deal with partial and noisy information about
their environment, to cope with uncertain situations and to find solutions to complex prob-
lems. The present paper aims at reviewing these mechanisms that are by now a stimulating
source of inspiration, especially when it comes to design distributed optimization algorithms
in computer science or control algorithms in collective robotics (Bonabeau and Theraulaz
2000). Implementations in artificial systems of this swarm intelligence logic are nowadays
numerous: discrete optimization (Dorigo et al. 1996,1999), graph partitioning (Kuntz et al.
1999), task allocation (Campos et al. 2000; Krieger et al. 2000), object clustering and sorting
(Melhuish et al. 2001; Wilson et al. 2004), collective decision making (Garnier et al. 2005),
and so on.
All these examples rely on mechanisms known to occur in social insects. However, if
social insects remain the original source of inspiration for artificial swarm intelligent sys-
tems it is important to notice that other biological systems share similar collective properties
Swarm Intell (2007) 1: 3–31 5
Fig. 1 Classification of
collective behaviors in social
insects. aAn external view and a
cross section of an Apicotermes
lamani nest resulting from the
coordination of workers building
activities. bCollective selection
of one foraging path over a
diamond-shaped bridge leading
to a food source by workers in
the ant Lasius niger.cWeave r
ant (Oecophylla longinoda)
workers cooperate to form chains
of their own bodies, allowing
them to cross wide gaps and pull
leaves together. dAn example of
division of labor among weaver
ant workers (Oecophylla
longinoda). When the leaves
have been put in place by a first
group of workers, both edges are
connected with a thread of silk
emitted by mature larvae held by
a second group of workers. ©
CNRS Photothèque Gilles Vidal
and Guy Theraulaz
such as colonies of bacteria or amoeba (Ben-Jacob et al. 1994,2000), fish schools (Grün-
baum et al. 2005; Parrish et al. 2002), bird flocks (Reynolds 1987), sheep herds (Gautrais
et al. 2007) or even crowds of human beings (Helbing et al. 2001). Among them, the mo-
tions of fish schools and bird flocks have for instance partly inspired the concept of particle
swarm optimization (Kennedy and Eberhart 1995). Nevertheless, we will restrict this re-
view to collective behaviors of social insects for at least two reasons: (1) they represent the
6 Swarm Intell (2007) 1: 3–31
largest research corpus from both a theoretical and an experimental point of view; (2) their
underlying principles are very close to those found or hypothesized in other animal species.
In this paper, we first describe in an historical perspective the basic mechanisms that
explain the amazing collective abilities of insect societies. This part is illustrated with well-
known examples and introduces the major concepts underlying the swarm intelligence re-
search field: decentralization, stigmergy, self-organization, emergence, positive and negative
feedbacks, fluctuations, bifurcations. It also highlights the nature of the relation between the
behavior of the individual and the behavior of the group, an idea of great importance for
understanding the third part of the paper.
In a second part, we introduce a categorization of these collective behaviors. This cat-
egorization is based on the interplay of four components that emerge at the level of the
group from the interactions and behaviors of the insects. We name these four components:
coordination, cooperation, deliberation and collaboration. We illustrate their role in the orga-
nization of a colony’s activities through various examples taken from the literature published
over the last 40 years.
The third part is dedicated to a problem which is central to swarm intelligence: the adap-
tation of the group to changes in the environment or in the composition of the group itself.
We argue that this adaptation can be the result of an active modulation of individual insects’
behaviors. In support of this argumentation, we provide three examples that cover three
different kinds of swarm intelligent problem solving: division of labor, morphogenesis and
collective decisions. We show in each case how small behavioral modifications participate
to the overall adaptation of the colony to changeable life conditions.
Finally, the last part opens a discussion about the need to better understand the role of
individual behavioral modulations in relation with the diversity of collective structures that
a colony of insects is able to produce. It also provides some keys that could inspire further
developments in the swarm intelligence research field.
2 The underlying mechanisms of complex collective behaviors
For a long time, the collective behavior of social insects has remained a fascinating issue
for naturalists. Everything happens as if there was some mysterious virtual agent inside the
colony that would coordinate the individuals’ activities. Even today, success novelists like
Michael Crichton have revived the old idea of the spirit of the hive (which was originally
introduced by the Belgian poet Maurice Maeterlinck 1927); in his novel “Prey”, Crichton
describes a swarm of artificial insect-like nanorobots which is governed by such a collective
mind, allowing them to take complex decisions and even to anticipate future events (Crich-
ton 2002). Of course, we know that there is no such spirit in the hive. Reality is less trivial,
and also much more interesting.
The quest for the mechanisms underlying insects’ collective behaviors started more than
a century ago and the first hypothesis put forward were clearly anthropomorphic (see for
instance Büchner 1881;Forel1921). Individual insects were assumed to possess something
like a representation of the global structure to be produced and then they were supposed to
use that representation to make appropriate decisions (see, for instance, Thorpe 1963). In
other words, people were thinking that there was some direct causal relationship between
the complexity of the decisions and patterns observed at the colony level and the behavioral
and cognitive complexity that was supposed to be required at the individual level to produce
these decisions and patterns. In particular, the queen was supposed to gather and monitor all
the information coming from its colony and then supervise the work done by the workers,
Swarm Intell (2007) 1: 3–31 7
giving them appropriate orders. For instance, Reeve and Gamboa (1983,1987) have argued
that in colonies of the paper wasp Polistes fuscatus the queen functions as a central pace-
maker and coordinator of workers activities. However, recent findings by (Jha et al. 2006)
demonstrated that indeed this original statement was wrong. The kind of organization that
was supposed to rule the society was hierarchical and centralized.
Nevertheless, most of the works that have been done in the last 40 years revealed a
completely different organization (Theraulaz et al. 1998a). We now know that individual
insects do not need any representation, any map or explicit knowledge ofthe global structure
they produce. A single insect is not able to assess a global situation, to centralize information
about the state of its entire colony and then to control the tasks to be done by the other
workers. There is no supervisor in these colonies.
A social insect colony is rather like a decentralized system made of autonomous units that
are distributed in the environment and that may be described as following simple probabilis-
tic stimulus-response behaviors (Deneubourg et al. 1983). The rules that govern interactions
among insects are executed on the basis of local information that is without knowledge of
the global pattern. Each insect is following a small set of behavioral rules. For instance, in
ants each individual is able to perform 20 different elementary behaviors on average (Wilson
1971). Organization emerges at the colony level from the interactions that take place among
individuals exhibiting these simple behaviors. These interactions ensure the propagation
of information through the colony and they also organize the activity of each individual.
Thanks to these sophisticated interaction networks, social insects can solve a whole range
of problems and respond to external challenges in a very flexible and robust way.
2.1 Stigmergy
The first serious theoretical explanation to the organization of social insects’ activities was
provided 40 years ago by French biologist Pierre-Paul Grassé, who introduced the concept of
stigmergy to explain building activity in termites (Grassé 1959; see Theraulaz and Bonabeau
1999 for an historical review). Grassé showed that the coordination and the regulation of
building activities do not depend on the workers themselves, but are mainly achieved by
the nest structure. In other words, information coming from the local environment and the
work in progress can guide individual activity. For instance, each time a worker performs a
building action, the shape of the local configuration that triggered this action is changed. The
new configuration will then influence other specific actions from the worker or potentially
from any other workers in the colony. This process leads to an almost perfect coordination
of the collective work and may give us the impression that the colony is following a well-
defined plan.
A good example of stigmergic behavior is provided by nest building in social wasps. The
vast majority of wasp nests are built with wood pulp and plant fibers that are chewed and
cemented together with oral secretions (Wenzel 1991). The resulting paper is then shaped
by the wasps to build the various parts of the nest: the pedicel, which is a stalk-like structure
connecting the comb to the substrate, the cells or the external envelope.
Building activities are driven by the local configuration of cells detected by the wasps
on the nest (Karsai and Theraulaz 1995). Indeed, the architecture by itself provides enough
information and constraints to ensure the coordination of the wasp building activity. To de-
cide where to build a new cell, wasps use the information provided by the local arrangement
of cells on the outer circumference of the comb. They perceive these configurations of cells
with their antennae. Potential building sites on the comb do not have the same probability
to be chosen by wasps when they start to build a new cell. Wasps have a greater probability
8 Swarm Intell (2007) 1: 3–31
Fig. 2 A model of stigmergic nest construction in wasps. Simulation of collective building on a 3D hexagonal
lattice (right). This architecture is reminiscent of natural Chartergus wasp nests (left) and exhibits a similar
design. A portion of the external envelope has been partly removed to show the internal structure of the nest
to add new cells to a corner area where three adjacent walls are already present, while the
probability to start a new row, by adding a cell on the side of an existing row, is very low
(Camazine et al. 2001).
The consequences of applying these local rules on the development of the comb and its
resulting shape can be studied thanks to a model in which wasps are represented by agents
(Theraulaz and Bonabeau, 1995a,1995b). These virtual wasps are asynchronous automata
that move in a three-dimensional discrete hexagonal space, and that behave locally in space
and time on a probabilistic stimulus-response basis. They only have a local perception of
their environment where a virtual wasp perceives the first twenty six neighboring cells that
are adjacent to the cell she occupies at a given time, and of course, this virtual wasp does
not have any representation of the global architecture she is supposed to build.
Each of these virtual wasps uses a set of construction rules. As they move in space,
they will sometimes come into contact with the nest structure and at this moment they will
perceive a local configuration of cells. Some of these configurations will trigger a building
action, and as a consequence, a new cell will be added to the comb at the particular place that
was occupied by the wasp. In all the other cases no particular building action will take place
and the wasp will just move toward another place. These construction rules are probabilistic,
so it is possible to use in the model the probability values associated with each particular
configuration of cells that have been measured in the experiments with the real wasps.
Nest architectures obtained by simulations show that the complexity of the structures
that are built by social insects does not require sophisticated individual behavioral rules (see
Fig. 2).
2.2 From stigmergy to self-organization: path selection in ant colonies
Another example of stigmergic behavior is food recruitment in ants (Hölldobler and Wil-
son 1990). Ants communicate with each other through the use of pheromones. These
pheromones are chemical substances that attract other ants. For instance, once an ant has
found a food source, she quickly comes back to the nest and lays down a pheromone trail.
This trail will then guide other workers from the nest toward the food source. When the
recruited ants come back to the nest, they lay down their own pheromone on the trail and
reinforce the pathway. The trail formation therefore results from a positive feedback: the
Swarm Intell (2007) 1: 3–31 9
more ants use a trail, the more attractive the trail becomes. Of course the trail will disappear
after some time if the reinforcement is too slow, which may occur when the food source
becomes exhausted. The interesting thing is that this trail recruitment system is not only a
mechanism used to quickly assemble a large number of foragers around a food source, it
also enables a colony to make efficient decisions such as the selection of the shortest path
leading to a food source.
In the beginning of the 1990s, Jean-Louis Deneubourg and his collaborators have de-
signed a simple and elegant experiment showing that information can be amplified and
selected by ant colonies using pheromone trails (Deneubourg and Goss 1989). In the ex-
periment, an ant nest was connected to a food source with a binary bridge whose branches
were of equal length. After a certain period of time, they observed that most traffic occurs
on a single branch. The choice was random with approximately 50% of the experiments in
which one branch was selected and 50% in which the other branch was selected. Initially,
the ant’s choice is made at random because there is no pheromone on the branches. As
time goes by, the stochasticity of individual decisions causes a few more ants to choose one
branch. The greater number of ants on this branch induces a greater amount of pheromone,
which in turn, stimulates more ants to choose the branch. This is a positive feedback which
amplifies an initial random fluctuation. In the end, most traffic will take place on a single
branch, chosen randomly (see Fig. 1(b)).
Today, we know that most collective decisions in social insects arise through the com-
petition among different types of information that can be amplified in various ways. In the
case of path selection by ants, the information is conveyed by the pheromone trail. However,
environmental constraints, such as the distance between the nest and the food source, affect
this positive feedback. In particular, any constraint that modulates the rate of recruitment
or the trail concentration on a branch can lead that branch to lose, or win, its competition
against the other one (Detrain et al. 2001; Jeanson et al. 2003). Thus, an efficient decision
can be made without any modulation of individual behavior and without any sophisticated
cognitive processing at the individual level.
This occurs, for example, when a colony of ants is presented with a short path and a
long path leading to a food source (Goss et al. 1989). Using the trail-laying trail-following
behavior, the shortest branch is selected in most cases. The first ants use both paths to reach
the food source. When they come back to the nest, the ones that take the shortest path
reach the nest first. The shorter path is thus slightly more marked with pheromone, and
is therefore, more attractive to the ants that leave the nest to go to the food source. In this
case, the positive feedback amplifies an initial difference induced by the path geometry. This
simple experiment shows that geometrical constraints can play a key role in the collective
decision-making processes that emerge at the collective level. The colony “as a whole” is
able to produce an efficient collective response that far exceeds the scale and abilities of a
single individual ant.
2.3 Principles and properties of self-organizing processes
These collective decisions in ants rely on self-organization that appears to be a major com-
ponent of a wide range of collective behaviors in social insects, from the thermoregula-
tion of bee swarms to the construction of nests in ants and termites (Bonabeau et al. 1997;
Camazine et al. 2001). Self-organization is a set of dynamical mechanisms whereby struc-
tures appear at the global level of a system from interactions among its lower-level compo-
nents, without being explicitly coded at the individual level. It relies on four basic ingredi-
ents:
10 Swarm Intell (2007) 1: 3–31
(1) The first component is a positive feedback that results from the execution of simple
behavioral “rules of thumb” that promote the creation of structures. For instance, trail
recruitment to a food source is a kind of positive feedback which creates the conditions
for the emergence of a trail network at the global level.
(2) Then we have a negative feedback that counterbalances positive feedback and that leads
to the stabilization of the collective pattern. In the example of ant foraging, negative
feedback may have several origins. It may result from the limited number of available
foragers, the food source exhaustion, and the evaporation of pheromone or a competition
between paths to attract foragers.
(3) Self-organization also relies on the amplification of fluctuations by positive feedbacks.
Social insects are well known to perform actions that can be described as stochastic.
Such random fluctuations are the seeds from which structures nucleate and grow. More-
over, randomness is often crucial, because it enables the colony to discover new solu-
tions. For instance, lost foragers can find new, unexploited food sources, and then recruit
nest mates to these food sources.
(4) Finally, self-organization requires multiple direct or stigmergic interactions among in-
dividuals to produce apparently deterministic outcomes and the appearance of large and
enduring structures.
In addition to the previously detailed ingredients, self-organization is also characterized by
a few key properties:
(1) Self-organized systems are dynamic. As stated before, the production of structures as
well as their persistence requires permanent interactions between the members of the
colony and with their environment. These interactions promote the positive feedbacks
that create the collective structures and act for their subsistence against negative feed-
backs that tend to eliminate them.
(2) Self-organized systems exhibit emergent properties. They display properties that are
more complex than the simple contribution of each agent. These properties arise from
the nonlinear combination of the interactions between the members of the colony.
(3) Together with the emergent properties, non linear interactions lead self-organized sys-
tems to bifurcations. A bifurcation is the appearance of new stable solutions when some
of the system’s parameters change (see Appendix 1). This corresponds to a qualitative
change in the collective behavior.
(4) Last, self-organized systems can be multi-stable. Multi-stability means that, for a given
set of parameters, the system can reach different stable states depending on the initial
conditions and on the random fluctuations.
3 Categorizing the collective behaviors of social insects
From the previously described self-organizing processes may emerge a wide variety of col-
lective behaviors that are intended to solve a given problem. Such diversity may give the
impression that no common point exists at the collective level between for instance the con-
struction of the relatively simple nest of the ant Leptothorax albipennis made up with a
single wall of debris and the construction of the seemingly more complex nest of the termite
Macrotermes bellicosus with its intricate network of galleries and chambers. Nevertheless, it
is possible to break down all these collective behaviors into a limited number of behavioral
components.
For example, Anderson and Franks (2001) have proposed to separate the collective be-
haviors accomplished by an insect colony into four task types: individual, group, team and
Swarm Intell (2007) 1: 3–31 11
partitioned tasks. Following that categorization of social insects’ behaviors, Anderson et al.
(2001) have proposed that every global task in a colony (for instance nest construction) can
be broken down in a hierarchical structure of subtasks of the previous types. Their method
can be seen as the deconstruction of a problem into the basic tasks required to solve it.
Another way to deconstruct the collective behaviors of social insects goes through the
functions that organize the insects’ tasks. We identified four functions of that kind: coordi-
nation, cooperation, deliberation and collaboration (see Fig. 1). They are not mutually exclu-
sive but rather contribute together to the accomplishment of the various collective tasks of
the colony. In the following sections, we first provide a definition of each and then illustrate
their respective role in some examples of social insects’ collective behaviors.
3.1 Coordination
Coordination is the appropriate organization in space and time of the tasks required to solve a
specific problem. This function leads to specific spatio-temporal distributions of individuals,
of their activities and/or of the results of their activities in order to reach a given goal.
For instance, coordination occurs in the organization of the displacement in bee and lo-
cust swarms (Buhl et al. 2006;Jansonetal.2005). In this case, the interactions between
individuals generate synchronized (temporal organization) and oriented (spatial organiza-
tion) movements of the individuals toward a specific goal.
Coordination is also involved in the exploitation of food sources by pheromone trail
laying ants. They build trail networks that spatially organize their foraging behavior between
their nest and one or more food sources (Hölldobler and Wilson 1990; Traniello and Robson
1995; Wilson 1962).
As a last example, coordination is at work in most of the building activities in insect
colonies. During nest building in certain species of social wasps (Downing and Jeanne,
1988,1990; Karsai and Theraulaz 1995; Wenzel 1996) or termites (Bruinsma 1979; Grassé
1959), the stigmergic process described in Sect. 2.1 favors the extension by an individual
of structures (spatial organization) previously (temporal organization) achieved by other
individuals.
3.2 Cooperation
Cooperation occurs when individuals achieve together a task that could not be done by
a single one. The individuals must combine their efforts in order to successfully solve a
problem that goes beyond their individual abilities.
Cooperation is obvious in large prey retrieval, when a single individual is too weak to
move a food item. Many cases of cooperative transport of prey were reported for several
ant species such as weaver ants Oecophylla longinoda (Wojtusiak et al. 1994), army ants
Eciton burchelli (Franks 1986)orFormi c a wood ants (Chauvin 1968; Sudd 1965). Such
cooperative transport of prey can be a very efficient way to bring back food to the nest.
For example, in the ant Pheidologeton diversus, it was reported that ants engaged in the
cooperative transport of a prey can hold at least ten times more weight than did solitary
transporters (Moffett 1988).
Cooperation can also be involved in other tasks than prey retrieval. For instance, it is
at work in chain formation in the weaver ant Oecophylla longinoda. In this ant species
individuals hang to each other to form chains allowing the bridging of empty space between
two branches or the binding of leaves during nest construction (Deneubourg et al. 2002;
Hölldobler and Wilson 1990; Lioni et al. 2001).
12 Swarm Intell (2007) 1: 3–31
A last example of cooperation occurs when a long wood stick is plug into the entrance of
an ant nest (Chauvin 1971). In such situation, ants combine their efforts to pull out the stick
from the hole. Some of the ants lift the stick up while others slip their head inside the hole,
in order to prevent the stick to fall back. Eventually, the combined efforts lead the group to
remove the stick from the nest entrance.
3.3 Deliberation
Deliberation refers to mechanisms that occur when a colony faces several opportunities.
These mechanisms result in a collective choice for at least one of the opportunities.
For instance, honeybees (Apis Mellifera) select the more productive floral parcels thanks
to the recruitment of unemployed workers by the waggle dance performed by foragers re-
turning from a food source (Seeley et al. 1991).
When ants of the species Lasius niger have discovered several food sources with different
qualities or richness, or several paths that lead to a food source, they generally select only
one of the different opportunities. In this case, the deliberation is driven by the competition
between the chemical trails leading to each opportunity (see Sect. 2.2). In most cases, ants
will forage at the richer food source and travel along the shorter path toward the food source
(Beckers et al. 1990,1992;Gossetal.1989).
3.4 Collaboration
Collaboration means that different activities are performed simultaneously by groups of spe-
cialized individuals, for instance foraging for prey or tending brood inside the nest (Gordon,
1989,1996; Wilson 1971). This specialization can rely on a pure behavioral differentiation
as well as on a morphological one and be influenced by the age of the individuals.
The most conspicuous expression of such division of labor is the existence of castes.
For instance, in leaf cutter ants workers may belong to four different castes and their size
is closely linked to the tasks they are performing (Hölldobler and Wilson 1990). Only the
workers whose head size is larger than 1.6 millimeters are able to cut the leaves that are
used to grow a mushroom that is the main food source of these colonies. On the contrary,
only the tiny workers whose head size is about 0.5 millimeters are able to take charge of the
cultivation of the mushroom.
Differently, all workers in Indian paper wasps Ropalidia marginata and Ropalidia cy-
athiformis, look alike. But they do not work to the same extent and they do not perform the
same kind of tasks. Some of the workers are foragers and take most of the burden of going
out of the colony in search of food and building materials. Others specialize in staying and
working at the nest. Among these, some are more aggressive towards their nest mates and
they are called fighters. The other wasps staying at home are called sitters and spend most
of the time just sitting and grooming themselves (Gadagkar and Joshi 1983,1984).
3.5 Organizing collective behaviors
Most of the collective behaviors in social insects can be understood as a combination of at
least two of the four functions of organization defined in the previous sections. To better
illustrate this point, we quickly describe in this section two examples of insects’ collective
behaviors and we break them down as coordination, cooperation, deliberation and collabo-
ration functions.
Swarm Intell (2007) 1: 3–31 13
3.5.1 House hunting in the honeybee
When a bee colony outgrows its hive, the mother queen and nearly half of the workers usu-
ally leave their nest. They temporarily form a cluster (called a bivouac) on a tree branch
from which they start a complex procedure for finding a new nest site (reviewed by Seeley
and Visscher 2004). First, scout bees (about 5% of the bees at the bivouac) explore the en-
vironment and search for suitable places to build a new hive. Once a scout bee finds such a
place, it comes back to the bivouac where it recruits some other scout bees by performing a
waggle dance. In turn, these recruited scouts assess the potential nest site and may possibly
perform the waggle dance to recruit other scouts. Thus, a competition arises between differ-
ent groups of scout bees recruiting for different potential nest sites. Once a site is visited by
a sufficient number of scout bees, these latter advertise the rest of the cluster that it is time
to warm up their flight muscles and to prepare for the liftoff toward the new nest site. They
use three distinct signals to that purpose: the shaking signal that activates the quiescent bees,
the piping signal that initiates the warm-up of the flight muscles and the buzz running signal
that prepares bees for the liftoff (Seeley and Tautz 2001).
At least three organization functions participate to the migration of honeybees toward a
new nest site. First, collaboration occurs since bees split in two different functional groups:
scout bees that search for potential nest site and clustered bees that remain quiescent and
conserve the colony’s energy reserves. Second, a self-organized deliberation process leads
to the choice of a suitable place for nesting among several opportunities. And third, the quasi
simultaneous liftoff of all bees in the cluster obviously results from a coordination function
mediated by the three liftoff preparation signals.
3.5.2 Nest construction in the weaver ant
Nest in the weaver ant Oecophylla longinoda are made of leaves stuck together (Hölldobler
and Wilson 1990; Ledoux 1950). The nest construction requires the repetition of two stages:
assembling two leaves and gluing them. In the first stage, workers line up in a row along
the margin of a leaf and pull together to bring closer the two leaves (see Fig. 1(c)). If the
gap between the two leaves is longer than a single ant, workers form a chain with their own
bodies. Then, they pull together as one individual to bring them closer. In the second stage,
when the leaves have been put in place, other workers carry mature larvae and use the silk
they produce to glue the leaves together (see Fig. 1(d)).
Nest construction in the weaver ant requires several functions of organization to be suc-
cessfully achieved. It first needs a coordination function to ensure that leaves are put together
before being glued. It also needs two cooperation functions. The first one occurs when work-
ers pull together the leaves since this task can not be performed by a single one. The second
arises during the gluing of the leaves: workers that do not produce silk require larvae that
can not move alone. At last, a collaboration function distributes the tasks between different
groups of individuals: workers that pull and maintain the leaves appropriately, workers that
carry the larvae and larvae that produce the silk for gluing the leaves.
3.5.3 Conclusion
As exemplified in the two previous subsections, the organization of collective behaviors in
social insects can be understood as the combination of the four coordination, cooperation,
deliberation and collaboration functions. Each of these functions emerges at the collective
level from the unceasing interactions between the insects. They support the information
processing abilities of the colony according to two main axes:
14 Swarm Intell (2007) 1: 3–31
(1) Coordination and collaboration shape the spatial, temporal and social structures that
result from the colony’s work. The coordination function regulates the spatio-temporal
density of individuals while the collaboration function regulates the allocation of their
activities.
(2) Cooperation and deliberation provide tools for the colony to face the environmental
challenges. The deliberation function represents the mechanisms that support the de-
cisions of the colony, while the cooperation function represents the mechanisms that
overstep the limitations of the individuals.
Together, the four functions of organization produce solutions to the colony problems and
may give the impression that the colony as a whole plans its work to achieve its objectives.
4 Modulation of self-organized behaviors
In the preceding sections, we have seen that the organization of the collective behaviors in
social insects arises from four functions that emerge from the activities of a dense network
of interactions. These interactions take place among the members of a colony and between
them and their environment. Because the colony and its environment permanently evolve in
time, they can be considered as coupled dynamic systems.
However, inside a colony of insects, some features seem to be actively maintained con-
stant and thus get out from the dynamic evolution of the colony. For instance, a humidity
drop can be life-threatening for cockroaches that could die as a consequence of desiccation.
To avoid the death, cockroaches maintain locally a sustainable humidity level thanks to an
increase of their tendency to aggregate (Dambach and Goehlen 1999). As another example,
when their colony size varies, ants of the species Leptothorax albipennis are able to maintain
their nest size so that each adult worker has about 5 mm2of floor area in the nest (Franks et
al. 1992). In these two cases, the colony modifies its behavior in order to counterbalance the
effects of a potentially harmful perturbation. The insect colony is thus an adaptive system.
Now the question is: what are the mechanisms that underlie collective adaptation in insects’
societies?
The only way for a colony to adapt its collective behavior is through the modulation of
individual insect behaviors. With the term “modulation” we suggest that the probability for a
given behavior to occur varies according to the disturbance. Each individual is able to sense
the variation thanks to local cues and it slightly modifies its behavior in response. These be-
havioral modifications affect the interaction network, and hence the global structure, through
a new balancing of positive and negative feedbacks.
4.1 Factors that modulate self-organized behaviors
Two kinds of perturbations modulate self-organized biological behaviors. The first ones are
produced by changes in the environment. We call them “outer colony” factors because they
arise independently of the insect colony. These factors comprise climatic parameters such
as temperature, humidity or wind, and ecological parameters such as food distribution or
predator presence.
We call “inner colony” factors the second kind of perturbations affecting self-organized
biological behaviors. These factors are directly linked to the colony or its components: the
size (i.e., the total number of individuals that belong to the colony), the morphological dif-
ferences between insects (i.e., the ratio of the different physical castes), learning, etc.
Swarm Intell (2007) 1: 3–31 15
Inner and outer factors both influence insect behavior. And the result of the insects’ ac-
tivities can, in return, influence outer and inner factors. For example, air flows modulate
the corpse clustering behavior of the ant Messor sanctus and are, in return, deviated by the
insects’ construction (Jost et al. 2007). The successful achievement of a task can improve
the experience of an individual which, in return, may favor the future achievement of the
same task (Theraulaz et al. 1991). Thus, a subtle network of interacting influences regulates
individual behaviors and provides to the colony a real-time solution to a real-time problem.
4.2 Three examples of modulation
Whatever the nature of the perturbation, one must answer the two following questions to
identify the mechanisms that underlie a collective adaptation to a given perturbation in an
insect colony.
(1) How does the perturbation modulate the individual behaviors of the interacting animals?
Does it stimulate animals to perform a particular behavior or task or does it prevent them
from doing so?
(2) How does the modulation of the individual behavior shape the properties at the collective
level? What changes does it trigger in the interaction network? What new structures does
it elicit when the group behavior is considered?
In order to better illustrate the previous questions, we present in the rest of this section, three
examples of self-organized biological behaviors and their modulation by inner and/or outer
factors. In each case we describe the mechanism in terms of individual behaviors, its result
at the collective level and the impact of the modulation of individual behaviors by one or
several factors.
4.2.1 Corpse clustering in ants
Numerous ant species carry their dead out of the nest and aggregate them near the nest
entrance (Ataya and Lenoir 1984; Haskins and Haskins 1974; Howard and Tschinkel 1976).
This behavior has been studied in the lab under controlled conditions with Messor sanctus
ants. When corpses are spread over the whole surface of an arena, ants collect and aggregate
the corpses within a few hours. In the beginning, several clusters are formed and compete
with each other to attract ants carrying corpses, and in the end only the piles that succeed to
grow faster than the others will persist (Theraulaz et al. 2002).
To build these structures, ants pick-up and drop a corpse as a function of the density of
corpses they detect in their local neighborhood. We can have an estimate of these probabil-
ities by looking at the behavior of ants when they come into contact with corpse piles of
increasing size. The greater the size of a pile, the less likely the ant will pick up a corpse on
that pile. On the other hand, the dropping probability increases rapidly as a function of the
size of the pile. Thus, a positive feedback results from the combination of both enhancement
of the dropping behavior and inhibition of the picking up behavior. This process is similar to
the one that led to the formation of foraging trails, with the difference that here the negative
feedback results from the depletion of isolated corpses that prevents the formation of other
clusters in the neighborhood of a cluster already in place.
In laboratory conditions with stable environmental factors, this corpse picking-up and
dropping behavior results in few persisting piles with a circular shape. Interestingly, in the
presence of a continuous laminar low speed air flow the form of the piles drastically changes.
From an almost circular shape they switch to an elliptic one: piles are elongated to create
16 Swarm Intell (2007) 1: 3–31
Fig. 3 Spatio-temporal dynamics of corpse clustering by ants Messor sanctus without (a) and with (b)air
currents. Black dots are ant corpses and black arrows indicate the air flow direction (from Jost et al. 2007
with permission)
parallel “walls” in the same direction as the wind (see Fig. 3). Note that the air flow is too
weak (around 1 cm s−1) to move a single corpse and thus cannot explain pile elongation by
its own. Something must have changed in the behavior of corpse carrying ants.
A recent work has investigated these individual behavioral changes and has linked them
to the observed shape modification in the presence of an air flow (Jost et al. 2007). Jost
and his collaborators have shown that the probability to pick up a corpse from a pile grows
with air flow speed while the probability to drop a corpse decreases with it. They, therefore,
conclude that the lower the wind speed, the higher the amplification of corpse clustering.
This leads ants to clear corpses from areas of high wind speed and to aggregate them in areas
of low wind speed. In addition, they showed with numerical simulations that corpse piles
modify the air flow around them: they slow it down on the side facing the wind (front side)
and on the lee side; they accelerate it on the other sides. Together with the previous result,
this explains why corpse piles lengthen in the same direction as the wind: amplification of
clustering is stronger at the front and lee side of the piles. Consequently, a pile will grow
from these two sides and will be elongated in the same direction as the wind.
This example illustrates the modulation of a self-organized behavior by an outer colony
factor through the modification of individual behavior. Ants aggregate corpses in piles that
locally modify air flow. This modification triggers a modulation of individual probabilities
to pick-up and drop corpses around the piles. The result is the appearance of a new spatial
structure.
4.2.2 Division of labor in wasps
Self-organized processes are also a major component of the division of labor in a colony. For
instance in Polistes wasps, division of labor is based on behavioral castes, and the task allo-
cation process results from a complex set of interactions among insects and the brood state
(Theraulaz et al., 1991,1992). In the Polistinae, sudden changes occur in the organization
of work when colony size increases (Karsai and Wenzel 1998).
We have experimental evidence showing that each insect has different response thresh-
olds for each of the different tasks to be done (see for instance Deneubourg et al. 1991 for
ants; Robinson 1992 and Pankiw et al. 2001 for bees; Weidenmüller 2004 for bumblebees).
These response thresholds control the probability for a wasp to perform a task. It depends
both on the threshold value and the stimulus level associated with the task at a given time.
Swarm Intell (2007) 1: 3–31 17
Moreover, these response thresholds change with the wasp’s experience (Theraulaz et al.
1991). The more an individual works on a task, the lower becomes the threshold for that
task. As a consequence, the wasp will be more responsive to small variations of the stimuli.
This is another example of a positive feedback loop. Conversely, if the wasp is not perform-
ing the task, because she is working on a different task or doing nothing, the threshold will
increase. This means that the wasp will be less responsive to the stimuli and the probabil-
ity to perform the task will be lower. This induces a negative feedback. Similar processes
have been described in bumblebees in the context of nest thermoregulation (Weidenmüller
2004). So, when several insects are in competition to perform a task, the combination of
the response threshold reinforcement and the competition among insects to perform the task
induces the differentiation of individuals and thus the division of labor at the colony level
(Theraulaz et al. 1998b). This mechanism creates not only a differentiation among individ-
uals, but it also ensures the adjustment of the ratio of workers engaged in the various tasks
and then plasticity of the division of labor.
Now, why do we observe an increase of differentiation among individual activity levels
when colony size is increasing? In other words, how does colony size (inner colony factor)
modulate the individual behavior of wasps so that the allocation of building tasks strongly
differs between small and large colony?
Theoretical results have shown that when the total amount of workload is proportional
to the size of the colony (that is, when the mean workload per individual remains constant),
there exist large fluctuations of the task associated stimuli in a small colony (see Fig. 4,
Appendix 1). As a consequence the positive feedback loops are impeded by the high level of
noise and individuals do not differentiate. On the contrary, the greater is the colony size, and
thus the higher is the absolute value of the number of tasks to be done, the smaller become
the fluctuations of the stimuli, and therefore, the greater are the chances that some of the
individuals develop in hard workers (Gautrais et al. 2002). The whole process does not only
induce a differentiation in the activity levels among insects, it also induces the specialization
of the hard workers in specific tasks. For instance in wasps, the normal sequence of tasks
involved in nest building first starts with collecting water, then the wasp searches for wood
pulp and finally she comes back to the nest where she molds the pellet and builds a cell. What
has been noticed is a general tendency for each wasp to be specialized in the execution of one
of these three tasks as the colony size is increasing (Karsai and Wenzel 1998). This means
that wasps specialized in water or pulp retrieval must exchange their collected material with
cell-building wasps when they come back to the nest. Thus, besides the specialization of
workers, the colony size increase also promotes a higher degree of coordination between
individuals.
In wasp colonies, division of labor and task specialization result from a combination of a
reinforcement process and a competition among individuals to perform the tasks. With these
processes the organization of division of labor is automatically adapted when colony size is
changing. When the total workload is high, which is the case in a large colony, it is better
to have specialized workers whose performance in task execution will be optimal instead of
having generalist workers with a lower performance. On the contrary, when the total amount
of work is small, which is the case in a small colony, it might be more relevant to have
generalist workers. Indeed, it would be too costly to keep specialized workers because these
specialists would not work frequently and so they would not be used in an optimal way.
So with this self-organized process, a colony is able to collectively optimize the division
of labor, with a minimal complexity of the behaviors and cognitive processes which are
required to achieve this regulation.
18 Swarm Intell (2007) 1: 3–31
4.2.3 Nest choice in ants
When the nest is destroyed or does not fulfill its requirements anymore (size, humidity, tem-
perature, etc.), ant colonies hunt for new opportunities to settle in their environment. Scout
ants seek for suitable places and then recruit their nest mates toward these locations. If
several suitable places exist, the colony has to make a choice between the alternatives. This
collective decision is done through the recruitment process. Two different recruitment mech-
anisms toward a new nest location are well studied in ants: mass recruitment and quorum
sensing.
Ants of the species Messor barbarus use a mass recruitment mechanism to select a new
nest site (Jeanson et al. 2004). This mechanism is similar to the one described in Sect. 2.2
in the context of foraging: nest location is selected thanks to a differential amplification of
the pheromone trails leading to the different places. The final choice of the colony can be
modulated by the quality of the potential nest sites. For instance, dark places are strongly
favored against light ones. Interestingly, this choice does not rely on a direct comparison
of the different opportunities by ants: facing two different potential nest locations, only 5%
of the scout ants that discover one of the two alternatives visit the other one (Jeanson et
al. 2004). In fact, the modulation of this collective choice relies on the modulation of the
individual trail laying behavior of ants: dark places increase intensity and frequency of trail
laying. This modulation of the individual behavior of ants paired with the amplification
process of the pheromone trail bias the final collective decision toward the choice of a dark
place. However, this situation differs from the differential choice of the shortest path toward
a food source seen in Sect. 2.2. In the case of nest choice, no environmental constraint acts on
the collective decision of the colony, this latter being only influenced by a natural preference
for dark places expressed by ants in the form of a variation of trail laying behavior.
Ants of the species Leptothorax albipennis use a completely different recruitment process
for choosing a new nest location. This recruitment process called “quorum sensing” takes
place in two successive steps (Pratt et al. 2002). First, scout ants that discover a suitable
place come back to the old nest and recruit a nest mate by leading a tandem run: the recruiter
slowly leads a single recruit from the old nest to the potential new nest. Here the recruited
ant assesses by itself the qualities of the potential nest place before recruiting a further nest
mate by leading its own tandem run. The quality of the nest modulates the duration of the
assessment period: a better nest is assessed more rapidly. It then induces a traffic flow which
grows more rapidly. Thanks to this modulation, the numbers of ants in the different potential
nest sites slowly diverge. Second, when the number of nestmates in one of the potential nest
sites rises above a “quorum” (i.e., the minimal number of individuals that must be present in
order for a decision to be taken) the recruitment behavior of ants in this place switches from
tandem runs to direct transport by simply carrying the passive nestmates from the former
nest. This recruitment by transport is three times faster than recruitment by tandem runs
(Pratt et al. 2002). The amplification of the initial choice is so important that the old nest is
moved to the new site before other potential sites reach the quorum. Therefore, the collective
choice of a nest site in Leptothorax albipennis is based on a threshold-based amplification
(also called quorum sensing) by the modulation of the individual recruitment behavior.
In the two examples of nest choice described above, modulation of individual behavior
by outer or inner colony factors deeply modifies the outcome at the collective level. In the
case of ants Messor barbarus the modulation of the trail laying behavior by environmental
conditions ensures that the colony “as a whole” compares the different opportunities and
chooses the best nest site whereas a very small number of ants actually visited all the alter-
natives. Without such a modulation, the colony would remain able to choose a nest site but
only at random.
Swarm Intell (2007) 1: 3–31 19
In quorum sensing by ants Leptothorax albipennis, the modulation of recruitment behav-
ior by the number of nestmates in the new nest is an essential part of the mechanism of the
decision: it provides the amplification process required to make the decision once the po-
tential nest site was evaluated and approved of by a sufficiently large number of nestmates.
Interestingly, this amplification mechanism can be also modulated to adapt the decision
making process of the colony to different environmental conditions. By decreasing the quo-
rum value, these ants are able to quicken the choice of a nest if the pressure for emigrating
is high (Franks et al. 2003). Conversely, if no particular pressure for emigrating exists, these
ants increase the quorum value and thus make more accurate the comparison between the
nests (Dornhaus et al. 2004).
5 Managing uncertainty and complexity with swarm intelligent systems
We have seen that complex colony-level structures and many aspects of the so-called swarm
intelligence of social insects can be understood in terms of interaction networks and feed-
back loops among individuals. These are the basic elements that allow the emergence of
dynamic patterns at the colony level. These patterns can be material (e.g., corpse clustering,
nest building) or social (e.g., division of labor) and lead the colony to structure its environ-
ment (e.g., during nest building) and solve problems (e.g., collective decision).
The most interesting properties of these self-organized patterns are robustness (the ability
for a system to perform without failure under a wide range of conditions) and flexibility (the
ability for a system to readily adapt to new, different, or changing requirements). Robustness
results from the multiplicity of interactions between individuals that belong to the colony.
This ensures that, if one of the interactions fails or if one of the insects misses its task, their
failure is quickly compensated by the other insects. This also promotes stability of produced
patterns whereas individual behaviors are mostly probabilistic.
Flexibility of self-organized systems is well illustrated by the ability of social insects
to adapt their collective behaviors to changing environments and to various colony sizes
(Deneubourg et al. 1986). These adaptations can occur without any change of the behavioral
rules at the individual level. For instance, in the case of the selection of the shortest path in
ants, a geometrical constraint applied on one of the two alternative paths increases the time
needed by the ants to come back to their nest through this path and thus biases the choice
toward the other path without any modification of the insects’ behaviors.
But flexibility can also rely on the modulation of the individual behavioral rules by some
factors produced by the environment or by the activity of the colony. For instance, the pres-
ence of an air flow modifies the probability for an ant to pick up and drop corpses of dead
ants. This subtle modification of behavioral probabilities deeply modifies the shape of the
piles resulting from the ants’ aggregating activity (see Sect. 4.2.1). As another example, the
modulation of the trail laying behavior as a function of the food source profitability in the
ants Lasius niger (Beckers et al. 1993)andMonomorium pharaonis (Sumpter and Beek-
man 2003) leads the colony to efficiently select the most rewarding food source if several
opportunities are discovered simultaneously. The nutrient demand of the colony can modify
the behavior of scout ants and can result in an adjustment of the harvesting strategy in the
ant Lasius niger (Portha et al., 2002,2004). The ability of a single worker to retrieve a prey
modifies its recruiting behavior and generates a diversity of collective foraging pattern in the
ant Pheidole pallidula (Detrain and Deneubourg 1997). Last, the presence of environmen-
tal heterogeneities can modulate the behavior of an insect and thus bias the behavior of the
colony toward a particular solution (Dussutour et al. 2005).
20 Swarm Intell (2007) 1: 3–31
All these behavioral modulations provide the opportunity for a colony to develop a wide
range of collective behaviors and can also be a powerful lever for evolution to shape and
optimize these behaviors in a highly adaptive way. Thanks to the sensitivity of individuals
to variations (either environmental or triggered by the colony itself), the colony as a whole
is able to perceive these changes and can thus react appropriately in almost every situation.
For the sake of simplicity, previous models of self-organized behaviors in insect so-
cieties often assumed that animals follow some kind of fixed behavioral rules and that
new collective structures appear after the system has reached a bifurcation point. If such
a viewpoint is of great interest to understand the mechanisms underlying a given col-
lective behavior, one must keep in mind that the natural context in which it occurs can
vary from day to day (or even from hour to hour) and that insect colonies have to
permanently adapt their behavior to changing conditions. For this reason, future stud-
ies in social insects should emphasize the role of individual behavioral modulations in
the flexibility of self-organized behaviors. Indeed these modulations trigger new inter-
esting questions about the way insect societies deal with unpredictable and complex en-
vironments. For instance, it would be interesting to know how many “modulated” indi-
viduals are required to significantly influence the collective output (Couzin et al. 2005;
Gautrais et al. 2004) or how much time is needed for the system to adapt its global be-
havior to the perturbations. These quantities can be viewed as the sensitivity and reactivity
of the system to the perturbations.
It would also be interesting to question at which conditions individual behavioral modu-
lations are efficient to produce flexible responses at the level of the colony (see Appendix 2).
Switching from a collective structure to another one which is better suited to the current sit-
uation requires at least that this switch is indeed possible. The following example illustrates
the problem. The black garden ant Lasius niger and the honey bee Apis mellifera both recruit
their nest mates toward newly discovered food sources. Scout ants use a pheromone trail to
lead uninformed workers to the food source while scout bees indicate its location thanks to
their well-known waggle dance. If one provides a bee or an ant colony with two different
food sources, a poor one and a rich one, at the same time, then ants and bees will be able
to select the richest one. But if one provides the poor source first, lets the colonies estab-
lish a recruitment toward this source and then introduces the rich source, then only the bees
will be able to switch their recruitment toward this new source (Camazine and Sneyd 1991).
Ants will continue to preferentially forage on the less rewarding food source (Beckers et al.
1990). The parameters of their recruitment mechanism do not allow them to change their
collective behavior to a more profitable one, as bees do.
This problem of ants being stuck in a less favorable collective behavior was addressed in
(Bonabeau 1996). Bonabeau showed with the help of a simple model of cooperative foraging
that the parameters of the recruitment behavior can lead to an efficient and flexible behavior
only if the corresponding stable state is on the one hand close to a bifurcation point and on
the other hand in a region where structures can appear and last. If the system at its stable state
is too far from a bifurcation point, it becomes hard to make it behave differently and it may
remain stuck in a sub-profitable solution. Thereby it should be relevant for a colony to have a
mechanism that keeps the collective output at the edge of a bifurcation, not too close so that
structures appear and are maintained, but also not too far so that they remain readily adaptive
(see Appendix 2). This could be the purpose of the modulations of individual behaviors.
Swarm Intell (2007) 1: 3–31 21
6 Conclusions
Understanding how self-organization works in social insects has already inspired numerous
algorithms to control the collective behavior of artificial systems (Bonabeau et al. 1999).
However, the recent discoveries about the role individual behavioral modulations play in the
adaptive abilities of insect colonies suggest us that the biological study of swarm intelligent
systems should provide new sources of inspiration for the design of control algorithms.
In particular, giving to artificial agents the ability to modulate their individual behaviors
according to cues partially reflecting at the individual level the modifications that occur at the
level of the colony would make this artificial colony better prepared to deal with uncertain
worlds. Such agents would be able to collectively anticipate negative side effects due to the
evolution of their own colony or to counterbalance the impact of hazardous environmental
changes.
The transition from “self-organization” to “self-organization plus self-adaptation” should
trigger an increase of complexity in the tasks or the environments an artificial colony could
deal with. The addition of self-adaptation to self-organization multiplies the number of
group patterns and collective behaviors that can be potentially displayed by the artificial
colony. Interestingly, this does not necessarily mean that the behavioral complexity required
at the agent level is also dramatically increased. Actually, the major modification of the in-
dividual behavioral controller should be a transition from fixed probabilities to accomplish
tasks to varying ones. This variation would be an appropriate function of a local cue that
correlates with the perturbation the global system would face.
In conclusion, the increased flexibility of collective structures in an insect colony trig-
gered by simple modulations of the individual behavior opens interesting ways toward the
design of self-adaptive artificial swarm intelligent systems. The pursuance of experimen-
tal investigations in biological systems and the development of new theoretical frameworks
about the adaptive role of these modulations should encourage the emergence of new ap-
plied studies. This lets us believe that the potential of swarm intelligence is far from being
exhausted.
Appendix 1 Bifurcations in self-organized behaviors
The dynamics of self-organized biological systems are shaped by the amplification of ran-
dom fluctuations through positive feedback loops. Such dynamical systems can display bi-
furcations in the space of solutions, depending on a driving parameter. Above a critical
value, the system can reach new stable states whereas the old solution becomes unstable.
In social insects, the dynamics of collective behaviors are intrinsically stochastic and
discrete but in some cases they can be approximated by a system of non-linear differential
equations, such as in the collective choice of a foraging path in ants.
Let us consider ants leaving their nest and facing a choice between two bridges leading
to the same food source. In the absence of clues, each ant will choose either of these bridges
with equal probability. If, however, preceding ants have left some pheromone on the bridge
they took, then the incoming ants prefer to walk on the bridge over which the pheromone
concentration is higher. This is a positive feedback loop: the more a bridge is chosen, the
more likely it will be chosen by ants.
For the very first ants facing the choice, the concentrations of pheromone on both bridges
are very low and their difference is difficult to assess. Hence, their choices are still more or
less equal between the two bridges. When the fluxes of ants reach a critical value, this
22 Swarm Intell (2007) 1: 3–31
difference becomes significant and triggers the positive feedback loop. Here, the flux of
incoming ants acts as a driving parameter.
Let C1and C2be the concentrations in pheromone on bridges 1 and 2. An ant leaving
the nest will face the two bridges and choose the bridge Ciwith probability:
p(Ci|C1,C
2)=(k +Ci)α
(k +C1)α+(k +C2)α,i=1,2
where kand αare parameters specific to the ant species and the actual set-up. If we assume
the simplification that an ant deposits one unit of pheromone each time it takes a bridge,
Cirepresents the flux of ants on the bridge i. The dynamics can thus be approximated by:
dCi
dt =Φp(Ci|C1,C
2)−μCi,i=1,2
where Φis the total flux of ants leaving the nest, and μthe characteristic time of pheromone
evaporation.
This equation can be adimensionalized using:
Ci→kci,
t→τ/μ,
φ→Φ/(kμ)
yielding
dci
dt =φ(1+ci)α
(1+c1)α+(1+c2)α−ci,i=1,2.
Hence, at the equilibrium, dci
dt =0, i=1,2, we have:
ci=φ(1+ci)α
(1+c1)α+(1+c2)α,i=1,2.
Experimental studies estimated α=2andk=4inLasius niger (Beckers et al. 1990,1992,
1993). Using these parameters, and c1+c2=φ, solutions are such that:
(ci−φ/2)c2
i−φci+1=0,i=1,2
which yields three equilibrium states:
(c1,c
2)=φ
2,φ
2,(1)
(c1,c
2)=1
2φ+φ2−4,1
2φ−φ2−4,(2)
(c1,c
2)=1
2φ−φ2−4,1
2φ+φ2−4.(3)
The solution (1) corresponds to an equal use of the two bridges: this is a no-choice
solution, whereas solutions (2)and(3) correspond to an asymmetrical use of the bridges:
they are a collective choice (Fig. 4A, B).
There is a bifurcation because solutions (2)and(3) only exist for φ>2.
Swarm Intell (2007) 1: 3–31 23
Fig. 4 A The stable solutions for the flux C1of ants on the bridge 1 (plain dots) as a function of the incoming
total flux φ. The system exhibits a bifurcation at φc=2 beyond which the no-choice solution becomes
unstable (open dots) and the fluxes become asymmetrical, either at a high level on bridge 1, or at a high level
on bridge 2. BThe differentiation of the flux on the two bridges occurs only above the critical total incoming
flux φc=2. The differentiation was computed as the absolute value of the relative difference of the fluxes
on each bridge shown in subfigure (A) above. CThe distribution of individual working times W(y-axis) for
increasing colony sizes (x-axis). In small colonies, all individuals work at the same rate (about 40% of their
time are devoted to the task), whereas for large colonies only a few individuals work at a high rate (75%) while
the others do not work much (20%). For each colony size, the distribution is an average over 1000 simulations
for 200 000 time steps. DThe differentiation W of the working times occurs only above a critical colony
size (N=20–30). For each simulation used for subfigure (C) above, the differentiation W (dots) was
computed as the difference of working time between the most working and the less working individuals in
the colony. The lines indicate the mean differentiation levels among the undifferentiated colonies (W ≈0)
and the highly differentiated colonies (W ≥0.3)
Hence φ=2 is the critical flux which elicits a bifurcation in the space of solutions: if the
total flux of ants leaving the nest is too low, the dynamics can not yield the collective choice
solutions.
Furthermore, beyond this critical value, the no-choice solution (1) becomes unstable.
Hence, the random fluctuations around the equal use of bridges will trigger the positive
feedback loop and lead the system to one of the two collective choice solutions. Since ants
24 Swarm Intell (2007) 1: 3–31
are actually a discrete system, one can be sure that the fluctuations are always sufficient for
destabilizing this no-choice solution.
Note that the adimensionalized variable representing the flux φis such that the corre-
sponding critical flux Φis proportional to the evaporation rate of the pheromone: the faster
the pheromone evaporates, the greater the minimal flux to yield a collective choice.
As a whole, the collective choice emerges provided that the flux of ants is sufficient to
compensate the pheromone evaporation.
In this first example, the bifurcation process leads the colony to drive all the individuals
to make eventually the same choice. As far as the individual behaviors are concerned, this is
a bifurcation which homogenizes the individual choices.
In other cases, the bifurcation can lead by contrast to break the homogeneity of the be-
haviors, as it is the case in a model of the division of labor in wasps’ colonies (Gautrais et
al. 2002).
In this model, the colony has to perform work for tackling a task T. Individuals can
engage in doing the task, performing αunits of work per unit of time. The task has an asso-
ciated stimulus Sthat can be perceived by all individuals. If no wasp is currently working,
the task spontaneously increases at a constant rate σ. Hence:
dS =(σ −K(t)α)dt, S(0)=0
where K(t) is the number of workers devoted to the task at time t. To simplify the compari-
son between different colony sizes, σis kept proportional to the colony size N.
For each individual i, the decision to perform the task is stochastic and depends on an
internal threshold Θi, according to:
P(i engages)=S2
S2+Θ2
i
.
If the threshold is low, ΘiS, individual iis very prone to engage in the task. Once
engaged in the task, the individual stops working at a constant rate p.
If all individuals have random threshold, the allocation of work among the workers would
simply reflect the underlying distribution of thresholds. However, this distribution would
have to be “well-shaped” for the system to fulfill the basic requirement of allocating the right
number of workers to the task and keep the stimulus at a minimal level. This well-shaped
distribution would depend on the balance between the increase of the amount of work to be
done (σ), the individual parameters (pand α) and the number of available workers, that is,
the size of the colony (N). Designing this distribution on an individual basis would require
that insects had access to global information (N, and the threshold of others) which is hardly
conceivable.
We proposed a mechanism of adaptive threshold that can produce such a distribution,
using only the information provided by the stimulus level.
Thresholds adapt according to the following rule: an individual engaged in the task gets
a lower threshold for the task whereas an idle individual gets a higher threshold for the task.
This positive feedback is expressed as:
dΘi=−ξIi+ϕ(1−Ii)dt
where Ii=1ifiis engaged in the task, 0 otherwise. (ξ , ϕ) are, respectively, thresholds
reinforcement and forgetting parameters. Thresholds are kept in a limited range from 0 to
Θmax that acts as a negative feedback which stabilizes the process.
Swarm Intell (2007) 1: 3–31 25
With this simple individual rule, a suitable distribution of thresholds spontaneously
emerges in a colony starting with all individuals having the same initial threshold (0).
For some values of (ξ, ϕ ), the system can furthermore exhibit a striking property of the
division of labor in social insects, namely that the individuals differentiate their working
time only in large colonies (Fig. 4C, D).
This size-induced bifurcation originates in the granularity of the stimulus fluctuations that
can trigger or not the positive feedback on the thresholds. In small colonies, the amounts σ
of work to be done, and αof work done by one individual are of the same order so that the
variation of the number of working individuals induces great variations of the stimulus. As a
consequence, even the workers with high thresholds have a significant probability to perform
the task when by chance the stimulus becomes low. On the contrary, in large colonies the
impact of the work done by one worker becomes negligible so the stimulus weakly fluctuates
around a constant value. As a consequence, the individuals with high thresholds have a
vanishing probability to perform the task (and their thresholds become even higher), and
only those workers that have a low threshold can be enrolled (and their thresholds stabilize
at a low value).
Appendix 2 Modulation of individual behaviors and collective response tuning
In the first approximation of the collective choice by ants presented in Appendix 1,the
individual choice function takes for granted that individuals can perceive pheromone levels
with no restriction:
p(c1)=(k +qc1)2
(k +qc1)2+(k +qc2)2(4)
where qrepresents the amount of pheromone left by an individual, c1,c2the fluxes on the
two bridges, and ka constant related to the perceptual discriminative power for pheromones.
However, we know that in general perceptual devices only respond to a limited range
of stimuli, and can saturate when the stimulus becomes too high. In the present case, this
can be taken into account by considering that the individuals estimate the flux on bridge ˜c1
through a saturating function of the actual amount of pheromone qc1. This function can be
modeled as:
qc1→˜c1=2cs(qc1)2
c2
s+(qc1)2(5)
where csrepresents the saturating value (Fig. 5A). Plugging (5)into(4) yields:
dCi
dt =Φ
(k +2cs(qci)2
c2
s+(qci)2)2
(k +2cs(qc1)2
c2
s+(qc1)2)2+(k +2cs(q c2)2
c2
s+(qc2)2)2−μCi,i=1,2(6)
which can have up to five real stationary solutions depending on the total adimensionalised
flux φ=Φ/μ.
Interestingly, for low values of φ, the system with perceptual saturation behaves similarly
as in the first approximation with no saturation, including the bifurcation to the collective
choice above a critical lower flux φm(compare Fig. 5B with Fig. 4A, Appendix 1). However,
for higher fluxes the perceptual saturation at the individual level prevents the emergence of
a collective choice at the colony level because both bridges appear equally attracting even
26 Swarm Intell (2007) 1: 3–31
Fig. 5 A Estimation ˜c1of the actual flux c1when the perceptual device is saturating at cs(arbitrarily fixed
to 10). Since the saturation pertains to the pheromone, ˜c1depends on the amount of pheromone qthat each
individual ant lays down. For higher values of q, the saturation occurs for lower values of the flux (from left to
right, q=4, 1.5, 1, 0.5). BThe stable solutions for the flux C1on the bridge 1 (plain dots) as a function of the
incoming total flux φ. The system exhibits the collective choice (asymmetrical fluxes) only for a range of φ.
The solutions presented correspond to q=1.5, marked as bold line in (A). CThe ranges of fluxes that elicit a
collective choice as a function of the individual deposit q. The collective response (z-axis) is indicated by the
asymmetry of the fluxes on the two bridges c =|c1−c2|
c1+c2.c > 0 indicates the emergence of the collective
choice
if the actual fluxes are not similar. Hence, there exists a critical upper flux φMabove which
the collective choice vanishes.
From a computational point of view, the collective choice can be considered as a response
of the colony to the environmental conditions, either external (as in the case of bridges of
different length) or internal such as the number of foragers.
Swarm Intell (2007) 1: 3–31 27
In the simplest case with no perceptual saturation, the system behaves as a high-pass
filter (it responds only to high incoming fluxes). Introducing the saturation constraint on
individual perceptual ability enables the system to behave like a bandpass filter: it responds
then to a specific range φm···φMof the total flux φ.
The bandwidth of this collective response depends non-linearly on the amount of
pheromone qleft by each individual (Fig. 5C). For high values of q, the system exhibits
a collective choice for lower values of φ, but saturates quickly. Correspondingly, for a given
flux φ, the collective choice can emerge only for a limited range of q. This range shrinks for
higher values of φ: an increasing number of individuals at play increases the accuracy of the
response. The collective response of the colony can thus be tuned by modulating individual
parameters.
From a functional point of view, the collapse of the collective choice for higher flux
values might be an unwanted side effect of the perceptual saturation. This collapse can be
prevented by an on-line modulation of the amount of pheromone qwhich is laid down by
ants. If the optimal regime is the one that maintains a collective choice for any flux values,
then a high value of qat the beginning of the recruitment process when the flux is low would
favor the emergence of the collective choice but it should decrease progressively as the flux
increases. This seems to be the strategy adopted by ants Lasius niger (Beckers et al. 1992).
Note the counter-intuitive trick of decreasing on-line the information left by each individual
in order for the colony to maintain its choice.
Acknowledgements We thank all the members of the EMCC group in Toulouse for many helpful discus-
sions and comments. We also thank the three anonymous reviewers for their useful advices. Simon Garnier
is supported by a grant from the French Ministry of Education, Research and Technology.
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