Essay: A Review on Basic Features of Swarm and Swarm Intelligent Systems

Abstract: Nature exhibits many surprising behaviors which have always attracted researchers towards them. One such behavior is of social insects which is known as ‘Swarm Intelligence’. The roots of swarm intelligence are deeply embedded in the study of self-organized behaviors in insects. These natural swarm intelligent systems have many features for example labor division, foraging behavior, synchronization, collective clustering and sorting, cooperative working etc. This paper focuses on some of the basic features of natural Swarm Intelligent systems along with their advantages and limitations.

Index Terms: self organization, stigmergy, pheromones.


Researchers are always inspired by the extremely diverse, dynamic, complex, robust and fascinating phenomenon exhibited by our mother nature. Nature has solution of each and every problem of our day-to-day life. It always maintains a perfect balance among its components and tends to solve problems in an adaptable and distributed manner. Also it has the ability to solve complex relationships through very simple rules. Among the various phenomenons shown by nature, the most fascinating phenomenon is the collective behavior of organisms.

In nature several animals tend to live in large groups. The reason is that in a group each animal is more effective for evolution than a single animal. For example, in a fish school chances of survival are higher for each individual of the swarm. A fish swimming alone is an easy target for predators, while on the other hand in a fish school predators usually have difficulties to single out a fish to attack. Moreover, the school has a much better perception of its environment; after all, many heads are better than one. This enables the fish to quickly react to predator attacks and to find food and, of course, mating partners more easily. Swarm Intelligence is a special field of Artificial Intelligence which is inspired by this behavior. Nowadays, this field is experiencing a profound increase in attention of researchers because of its amazing efficiency in almost every field of application.

The organization of paper as: Section II defines the term swarm and gives an overview of concepts underlying SI. Section III highlights features of SI. Advantages and limitations of SI are discussed in section IV. Conclusion is drawn in section V.


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 behavior to emerge[1]. Although these agents (insects or swarm individuals) are relatively unsophisticated with limited capabilities on their own yet they are interacting together with certain behavioral patterns to cooperatively achieve tasks necessary for their survival. These agents follow very simple rules, and there is no centralized control structure dictating how individual agents should behave. Such local and to a certain degree random interactions between the agents lead to the emergence of intelligent global behavior, known as ‘Swarm Intelligence’. Natural examples of swarm include ant colonies, bird flocks, bee colonies, fish schools etc.


A. Collective Behavior

Collective behavior can be defined as tasks performed by the individuals of the swarm collectively. These tasks can be non’cooperative or cooperative. Non’cooperative tasks are tasks which don’t need any kind of cooperation between the individuals performing the tasks and can be successfully performed by one single individual given unlimited time. Examples of such non-cooperative tasks are: sorting, searching, map making, harvesting, vacuuming. Cooperative tasks are tasks which can’t be completed by one single individual. They have to be performed collectively with two or more individuals working together. Examples of such cooperative tasks are: formation marching, material transport, tandem movement, nests building. Therefore, cooperation is the essential behavior of any type of swarm.

An example of collective swarm behavior is nest building in ants. Building a nest consists of two steps: First, the ants need to find leaves. The leaves are too heavy for one ant to handle, so the ants need to make a long line and pull the leaves together. If the leaves are still too heavy, more ants will help by making a second row of ants. They will connect with the ants in front of them, and they will together pull it as one bigger unit. The ants pull the leaf until it lies next to the other leaves. The next step is to use the silk which the larva produces for gluing the leaves together. Here also one single ant is not strong enough to carry one larva by itself. Therefore, collective behavior is a necessity of swarm.

B. Communication

Communication is the most fundamental property of a swarm that must be present for either competition or cooperation between individuals. Communication can be both direct and indirect. Direct communication is exchanging information between individuals either through touch, body language (behavior), force, symbolic elements or time spent. An example of direct communication is in the case of Honey bees. The bees communicate with each other through the language of dance. A bee can perform different type of dance, which gives different information.

The two most common dances are the round dance and the waggle dance. The round dance is often used when a food source is close to the hive, between 50 and 1500 meters away. The way a bee performs the round dance, is by walking around in circles, suddenly turning 180 degrees, and then start walking forwards. This round dance says that the food source is nearby, but it doesn’t say anything about where it is located. The waggle dance however is most known. This is a complex dance, which describe both the direction and the distance. The dance is performed by walking around in two small circles and shaking its body whenever it is in the middle. However, the main purpose of these dances is to recruit the other bees, and tell them where food sources or possible new nest sites are located.

The indirect type of communication in insects is also known as ‘Stigmergy’. Derived from the Greek word stigma (sting) and ergon (work), Pierre- Paul Grasse in 1950’s, introduced this term in his studies of task coordination and nest reconstruction of termites[2]. The concept of stigmergy is that an individual must always take into account the changes that have occurred in the surrounding environment and act on the basis of these changes. If one individual modifies the environment, the same individual or another individual get stimulated and responds to the changes by performing actions based on the new environmental settings.

Stigmergy is a necessity in any dynamic swarm system because of the scaling issue in direct communication. In stigmergy the insects communicate indirectly through the use of pheromones[2]. Pheromones are chemical signals sent out from one individual to trigger a reaction behavior on the receiving individuals of the same species. These chemical signals can be used in many different occasions; trail-following, defense, mating and retrieving food.

Ants base their communication on pheromones and detects them by using their antennas (similar to smell)[2]. Their antennas are equipped with features to both notice direction and intensity of pheromones. When an ant finds a new food source, it will lay down pheromones at the way back to the hill. Other ants will be attracted to the same path due to pheromone deposition. The intensity of the pheromone on the path created will be increased as more and more ants follow the same path. When the food source diminishes, fewer ants lay down pheromones, and the path will eventually disappear.

C. Division of Labor

Division of labor is both fundamental and necessary for a swarm to function effectively as a group. In social insects, different activities are often performed simultaneously by specialized individuals. Simultaneous task performance by specialized workers is believed to be more efficient than sequential task performance by unspecialized workers. Specialization results in greater efficiency of individuals in task performance because they ‘know’ the task or are better equipped for it.

All social insects exhibit reproductive division of labor: only a small fraction of the colony, often limited to a single individual, reproduces. Beyond this primary form of division of labor between reproductive and worker castes, there most often exists a further division of labor among workers, which may take three, possibly coexisting, basic forms[2]:

  • Temporal polytheism: – With temporal polytheism, individuals of the same age tend to perform identical sets of tasks. Individuals in the same age class form an age caste. It is not clear at the moment whether or not absolute ageing is the main cause of temporal polytheism. Social and external environment, as well as genetic characteristics, seems to influence the rate of behavioral ontogeny.
  • Worker polymorphism. In species that exhibit worker polymorphism, workers have different morphologies. Workers that differ by their morphologies are said to belong to different morphological or physical castes. Workers in different morphological castes tend to perform different tasks. An example of a worker caste is the soldier or major caste which is observed in several species of ants.
  • Individual variability. Even within an age or morphological caste, differences among individuals in the frequency and sequence of task performance may exist. One speaks of behavioral castes to describe groups of individuals that perform the same set of tasks within a given period.

A key feature of division of labor is its plasticity. Division of labor is rarely rigid. The ratio of workers performing the different tasks that maintain the colony’s viability and reproductive success can vary in response to internal perturbations or external challenges. Such factors as food availability, predation, climatic conditions, phase of colony development, or time of year influence the size and structure of a colony’s worker population in natural conditions. The worker force must be allocated tasks so as to adjust to challenging conditions. Changes in the pattern of task allocation can be induced experimentally by altering colony size, structure, or demography, or by increasing the need for nest maintenance, nest repair, defense, etc

D. Self – Organization

Self’organization is a process in which patterns at global level of a system emerge solely from numerous interactions among the lower-level components of the system[5]. Moreover, the rules specifying the interactions among the system’s constituent units are executed on the basis of purely local information, without reference to the global pattern, which is an emergent property of the system rather than a property imposed upon the system by an external ordering influence. For example, the emerging structure in the case of foraging spatiotemporally organized networks of pheromone trails.

The basic characteristics of self-organized systems are:

  • Interactions
  • Positive feedback
  • Negative feedback
  • Random fluctuations
  • Pattern Formation
  • Multistability
  • Bifurcations

Interaction is the only essential mechanism required for self-organization or swarm intelligence. The nature and extent of interactions between individuals varies, but their existence is a clear prerequisite for the complex system behavior evident in self-organized systems. In biological systems, these interactions often allow an individual to obtain the information used to determine a response. Gathering information from an interaction is generally a result of some type of communication with nearest neighbors. In the case of flocking (and many other examples), however, the local information obtained in each interaction is simply the position (and possibly velocity) of a handful of nearest neighbors[3]. This information is sensed directly; there is no need for each neighbor to communicate directly or indirectly. It is also unnecessary to leave some sort of environmental marker to communicate via the environment. In cases such as these, sensing information during an interaction is sufficient to produce complex system behavior. In most other cases, the information is gained through communication intended to convey information, or direct communication.

Positive feedback, or amplification, is a common mechanism in self-organized systems. Positive feedback promotes radical changes in the system by taking an initial change and reinforcing it in the same direction. An important example of positive feedback, or autocatalysis, is the trail-based foraging of ants. When a single forager ant discovers food, it leaves a pheromone trail while returning to the nest. Other ants follow this trail from the nest to the food source and reinforce the initial trail as they return home. In turn, more foragers leave the nest and so on. As a result of positive feedback, the pheromone concentration of the trail increases rapidly, as does the number of ants leaving the nest.

Negative feedback balances the effect of positive feedback and stabilizes collective patterns. The catalytic nature of positive feedback requires an opposing force in most cases. Otherwise, systems could commit unreasonable amounts of resources to a particular activity. In fact, negative feedback often occurs due to depletion of limited individual or system resources. Negative feedback takes forms such as saturation, exhaustion, overcrowding and/or competition. Returning to the example of foraging ants, negative feedback stems from a limited number of available foragers, colony-level satiation, food source exhaustion, local crowding at the food source, competition between food sources, and/or pheromone evaporation.

Random fluctuations are surprisingly common in biological systems that exhibit self-organization. Many of these systems actually rely on certain stochastic elements for behavioral flexibility. The amplification of these random fluctuations (random walks, errors, etc.) allows for the discovery of new solutions as well as acting as seeds from which new structures can nucleate and grow. An excellent example of this is caused by stochastic trail-following in ants. It is well- known that ants follow trails imperfectly, especially trails with low pheromone concentrations. When an individual lose the trail and becomes ‘lost’, it has the potential to ‘stumble across’ an undiscovered source of food. This could be a better (e.g., closer, richer, larger) food source than that currently being exploited by the colony. Clearly, random fluctuations are vital to efficient self-organization.

The emergent pattern formation i.e. creation of complex spatiotemporal structures in initially unstructured media is omnipresent in self-organized systems. The term ’emergent’ refers to a property that arises unexpectedly from nonlinear interactions among a system’s components and that cannot be understood as the simple addition of their individual contributions.

The possible coexistence of multiple possible stable states, or attractors, is known as multistability. Depending on the initial conditions in a self-organized system, there can be a number of stable states to which the system evolves. A range of initial conditions corresponding to a particular stable state is a basin of attraction for that state, or attractor. For example, in ant colonies utilizing mass recruitment where two equal food sources are present, the colony tends to exploit one of the two sources. There are two stable states; the one that is actually chosen depends partly on randomness, but also on whichever source is discovered first.

Dramatic changes in system behavior, or bifurcations, occur in many self- organized systems. Bifurcations are defined as ‘the appearance of a qualitative change in behavior when a parameter-value changes quantitatively’. In response to variations in certain system or environmental parameters, a system can switch from one state to another through bifurcation phenomena.

Nest building in termites is the typical example of Self-organized system. Termite workers use soil pellets, which they impregnate with pheromones to build pillars. Two successive phases take place during nest reconstruction. First, a non- coordinated phase occurs which is characterized by a random deposition of pellets. This phase lasts until one of the deposits reaches critical size. Then, a coordination phase starts if the group of builders is sufficiently large and pillars emerge. The existence of an initial deposit of soil pellets stimulates workers to accumulate more material though a positive feedback mechanism, since the accumulation of material reinforces the attractivity of deposits through the diffusing pheromone emitted by the pellets. This autocatalytic snowball effect leads to the coordinated phase. If the density of builders is too small, the pheromone disappears between two successive passages by the workers, and the amplification mechanism cannot work, which leads to a non-coordinated behavior. The system undergoes a bifurcation at this critical density: no pillar emerges below it, but pillars can emerge above it. This example therefore illustrates positive feedback (the snowball effect), negative feedback (pheromone decay), the amplification of fluctuations (pillars could emerge anywhere), multiple interactions (through the environment), the emergence of structure (i.e. pillars) out of an initially homogenous medium (i.e. a random spatial distribution of soil pellets), multistability (again, pillars can emerge anywhere) and bifurcation which make up the signatures of self-organized phenomena.


The formation of a swarm simultaneously provides the individual and the group a number of benefits arising from the synergy of interaction. As an agent in the swarm, the potential benefits available to the individual are maximized through the efforts of the others in the swarm. From the perspective of the swarm as an organism, the survivability of the swarm is increased through the higher-level coordination that emerges from the low level interactions of the individuals.

There are several examples in nature that exhibit the individual and group benefits of a swarm. First, swarming provides the ability to forage more effectively. The best known example is the foraging behavior of ants. Each ant searches for food, and upon finding a food source returns to the colony, emitting a pheromone trail along the way. Other ants detect the pheromone trail along the way and follow the trail to the colony, thus reinforcing the trail. As a result, the trail to the closest food source is the strongest, thus without any central supervision the ant colony has located the closest food source, thus optimizing their search.

Also, there is the concept of safety in numbers. A fish travelling in a school is much safer from predators than it would be travelling alone. When attacked, the members of a swarm may confuse predators through coordinated movements, such as rapid division into subgroups. Also, because of the large concentration of individuals in a swarm, the risk of injury to a predator, should it attack, can be much larger than when it is attacking a single animal. Swarming plays an efficient role in the case of reproduction: it is easier to find a mate in a large group. However, also in this case, there are both benefits and drawbacks, since competition obviously increases as well. Swarming is also a prerequisite for cooperation that, in turn, plays a crucial role in, for example, the foraging (food gathering) of several species, particularly ants, bees and termites, but also in other organism such as birds[5]. Here, the principle is that many eyes are more likely to find food than a single pair of eyes. Finally, travelling as a group maximizes the distance able to be traveled. For example, the V-shape formation observed in flocking birds is a functional formation where the flight of the lead bird reduces the drag on the rest of the flock, thus enabling the flock to travel farther as a whole.


Bonabeau [1] defined Swarm intelligence as ‘The emergent collective intelligence of groups of simple agents’. Swarm Intelligence (SI) can be described as the collective behavior emerged from the social interactions among individual agents which help them to adapt to the environment more efficiently since more information is gathered by the whole swarm. This emergent intelligence of a swarm system is very complicated which depends on factors like number of participants in a swarm system, intelligence level of each participant, type of interactions and communication and duration of the behavior.


A. Advantages

a) SI systems can solve complex problems through only simple and sometimes even binary interactions. This kind of cooperative behavior is a useful solution to handle unpredicted situations in our today interwoven computing systems.

b) The distributed nature of swarm groups maximizes the overall system dependability by removing critical challenges such as single points of failure, bottlenecks and unbalanced traffics.

c) SI systems are highly scalable i.e. the control mechanisms used in SI systems are not dependent on swarm size.

d) SI systems are adaptable to rapidly changing environments, making use of their inherit auto-configuration and self-organization capabilities.

e) SI systems are robust as they can collectively work together without central control and no single individual is crucial for the swarm to function.

B. Limitations

a) SI systems are not suitable for time-critical applications because the path to solution is neither pre-defined nor pre-programmed.

b) SI systems have limited communication.

c) Theoretical analysis of SI systems is difficult due to sequences of random decisions.

d) Solution is guaranteed but time for getting the solution is not defined.


There are lots of things to be learned from nature. It a great inspirational source for us. Many real life problems can be solved by taking inspiration from the behavior of natural swarms. Several individuals interacting locally among themselves can eventually emerge a global sophisticated behavior. This paper summarizes some prominent features of Swarm Intelligence paradigm. Swarm Intelligence is going to be new research area for scientists. The scope of SI methodologies is very vast with many researches being currently carried out on it but still its potential is far from being exhausted completely.


[1] M.Dorigo, E. Bonabeau, and G. Theraulaz, Swarm Intelligence, From Natural to Artificial Systems, Oxford University Press, 1999
[2] M.Dorigo, E. Bonabeau, and G. Theraulaz, Ant algorithms and stigmergy, Future Gener. Comput. Syst., Vol. 16, No. 8, pp. 851-871, 2000.
[3] Er. A.K. Mishra, Dr. M.N. Das and Dr. T.C. Panda, ‘Swarm Intelligence Optimization: Editorial Survey’, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, Issue 1, January 2013
[4] Rashmi A. Mahale and Prof.S.D.Chavan, ‘A Survey: Evolutionary and Swarm Based Bio- Inspired Optimization Algorithms’, International Journal of Scientific and Research Publications, Vol. 2,Issue 12, December 2012
[5] Binitha S and S Siva Sathya, ‘A Survey of Bio inspired Optimization Algorithms’, International Journal of Soft Computing and Engineering, Vol. 2,Issue 2,May 2012

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