54426609 Swarm Inteligence Seminar Report

Swarm Technology by Abhilash Nayak| 1

Swarm Technology (ST) is the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.

Swarm describes behaviour of an aggregate of animals of similar size and body orientation, often moving en masse or migrating in the same direction. Swarming is a general term that can be applied to any animal that swarms. The term is applied particularly to insects, but can also be applied to birds, fish, various microorganisms such as bacteria, and people. The term flocking is usually used to refer to swarming behaviour in birds, while the terms shoaling or schooling are used to refer to swarming behaviour in fish. The swarm size is a major parameter of a swarm.

I. INTRODUCTION ST is the property of a system whereby the collective behaviours of agents interacting

locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore distributed problem solving without centralized control or the provision of a global model. One of the cores tenets of SI work is that often a decentralized, bottom-up approach to controlling a system is much more effective than traditional, centralized approach. Groups performing tasks effectively by using only a small set of rules for individual behaviour is called swarm intelligence. Swarm Intelligence is a property of systems of no intelligent agents exhibiting collectively intelligent behaviour. In Swarm Intelligence, two individuals interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time. For years scientists have been studying about insects like ants, bees, termites etc. The most amazing thing about social insect colonies is that there’s no individual in charge. For example consider the case of ants. But the way social insects form highways and other amazing structures such as bridges, chains, nests and can perform complex tasks is very different: they self-organize through direct and indirect interactions. The characteristics of social insects are

1. Flexibility 2. Robustness 3. Self-Organization


II. PROPERTIES OF SWARM INTELLIGENCE The typical swarm intelligence system has the following properties:

• It is composed of many individuals; • The individuals are relatively homogeneous • The interactions among the individuals are based on simple behavioural rules

that exploit only local information that the individuals exchange directly or via the environment

• The overall behaviour of the system results from the interactions of individuals with each other and with their environment, that is, the group behaviour self-organizes.

III. MODELLING SWARM BEHAVIOUR The simplest mathematical models of animal swarms generally represent individual animals as following three rules:

1. Move in the same direction as your neighbour 2. Remain close to your neighbours 3. Avoid collisions with your neighbours

Many current models use variations on these rules, often implementing them by

means of concentric "zones" around each animal. In the zone of repulsion, very close to the animal, the focal animal will seek to distance itself from its neighbours to avoid collision. Slightly further away, in the zone of alignment, the focal animal will seek to align its direction of motion with its neighbours. In the outermost zone of attraction, which extends as far away from the focal animal as it is able to sense, the focal animal will seek to move towards a neighbour?


The shape of these zones will necessarily be affected by the sensory capabilities of the given animal. For example the visual field of a bird does not extend behind its body. Fish rely on both vision and on hydrodynamic perceptions relayed through their lateral line, while Antarctic krill rely both on vision and hydrodynamic signals relayed through antennae. Some of the animals that exhibit swarm behaviour are

1. Insects – Ants, bees, locusts, termites, mosquitoes and insects migration. 2. Bacteria 3. Birds 4. Land animals 5. Aquatic animals – fish, krill and other aquatic animals 6. People

IV. EXAMPLE ALGORITHMS OF SWARM INTELLIGENCE

v Ant colony optimization(ACO) v River formation dynamics v Particle swarm optimization(PSO) v Stochastic diffusion search v Gravitational search algorithm(GSA) v Intelligent Water Drops v Charged System Search

i. Ant Colony Optimization

Ant colony optimization (ACO) is a class of optimization algorithms modeled on the actions of an ant colony. ACO methods are useful in problems that need to find paths to goals. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee. In other words we can say that , the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. In the real world, ants wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food through that way.


This algorithm is inspired by forgiving behaviour of the ants.

1. The first ant finds the food source (F), via any way (a), then returns to the nest (N), leaving behind a trail pheromone (b)

2. Ants indiscriminately follow four possible ways, but the strengthening of the runway makes it more attractive as the shortest route.

3. Ants take the shortest route, long portions of other ways lose their trail pheromones.

Figure i ACO

In a series of experiments on a colony of ants with a choice between two unequal

length paths leading to a source of food, biologists have observed that ants tended to use the shortest route. A model explaining this behaviour is as follows:

1. An ant (called "blitz") runs more or less at random around the colony; 2. If it discovers a food source, it returns more or less directly to the nest, leaving in

its path 3. a trail of pheromone; 4. These pheromones are attractive, nearby ants will be inclined to follow, more or

less 5. directly, the track; 6. Returning to the colony, these ants will strengthen the route; 7. If there are two routes to reach the same food source then, in a given amount of

time, 8. the shorter one will be travelled by more ants than the long route; 9. The short route will be increasingly enhanced, and therefore become more 10. attractive; 11. The long route will eventually disappear because pheromones are volatile; 12. Eventually, all the ants have determined and therefore "chosen" the shortest

route.


Pseudo code of ACO 1 : repeat 2 : if antCount < maxAnts then 3 : create a new ant 4 : set initial state 5 : end if 6 : for all ants do 7 : determine all feasible neighbour states {considering the ant's visited states} 8 : if solution found V no feasible neighbour state then 9 : kill ant 10: if we use delayed pheromone update then 11: evaluate solution 12: deposit pheromone on all used edges 13: end if 14: else 15: stochastically select a feasible neighbour state {directed by the

ants memory, the pheromone concentration on the edges and local heuristics}

16: if we use step-by-step pheromone update then 17: deposit pheromone on the used edge 18: end if 19: end if 20: end for 21: evaporate pheromone until termination criterion satisfied {e.g., found a satisfying solution}

ii. River Formation Dynamics

River formation dynamics (RFD) is an heuristic method similar to ant colony optimization (ACO). In fact, RFD can be seen as a gradient version of ACO, based on copying how water forms rivers by eroding the ground and depositing sediments. As water transforms the environment, altitudes of places are dynamically modified, and decreasing gradients are constructed. The gradients are followed by subsequent drops to create new gradients, reinforcing the best ones. By doing so, good solutions are given in the form of decreasing altitudes. This method has been applied to solve different NP-complete problems (for example, the problems of finding a minimum distances tree and finding a minimum spanning tree in a variable-cost graph). The gradient orientation of RFD makes it especially suitable for solving these problems and provides a good tradeoff between finding good results and not spending much computational time. In fact, RFD fits particularly well for problems consisting in forming a kind of covering tree.


iii. Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each time step. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. Ex. Birds flocking

Figure ii PSO


Algorithm of PSO

As stated before, PSO simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do not know where the food is. But they know how far the food is in each iteration. So what's the best strategy to find the food? The effective one is to follow the bird which is nearest to the food. PSO learned from the scenario and used it to solve the optimization problems. In PSO, each single solution is a "bird" in the search space. We call it "particle". All of particles have fitness values which are evaluated by the fitness function to be optimized, and have velocities which direct the flying of the particles. The particles fly through the problem space by following the current optimum particles. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. In every iteration, each particle is updated by following two "best" values. The first one is the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the population. This best value is a global best and called gbest. When a particle takes part of the population as its topological neighbors, the best value is a local best and is called lbest. After finding the two best values, the particle updates its velocity and positions with following equation (a) and (b). v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[])----------(a) present[] = present[] + v[] ----------------------------------------------------- (b) Where:- v[] is the particle velocity, persent[] is the current particle (solution). pbest[] and gbest[] are defined as stated before. i.e. personal best and global best respv. rand () is a random number between (0,1). c1, c2 are learning factors. Usually c1 = c2 = 2.


The pseudo code of the procedure is as follows For each particle Initialize particle END Do For each particle Calculate fitness value If the fitness value is better than the best fitness value (pBest) in history set current value as the new pBest End Choose the particle with the best fitness value of all the particles as the gBest For each particle Calculate particle velocity according equation (a) Update particle position according equation (b) End While maximum iterations or minimum error criteria is not attained Particles' velocities on each dimension are clamped to a maximum velocity Vmax. If the sum of accelerations would cause the velocity on that dimension to exceed Vmax, which is a parameter specified by the user. Then the velocity on that dimension is limited to Vmax. Comparisons Between Genetic Algorithm And PSO Most of evolutionary techniques have the following procedure:

1. Random generation of an initial population 2. Reckoning of a fitness value for each subject. It will directly depend on the

distance to the optimum. 3. Reproduction of the population based on fitness values. 4. If requirements are met, then stop. Otherwise go back to 2.

From the procedure, we can learn that PSO shares many common points with GA. Both algorithms start with a group of a randomly generated population, both have fitness values to evaluate the population. Both update the population and search for the optimum with random techniques. Both systems do not guarantee success.


However, PSO does not have genetic operators like crossover and mutation. Particles update themselves with the internal velocity. They also have memory, which is important to the algorithm. Compared with genetic algorithms (GAs), the information sharing mechanism in PSO is significantly different. In GAs, chromosomes share information with each other. So the whole population moves like a one group towards an optimal area. In PSO, only gbest (or lbest) gives out the information to others. It is a one -way information sharing mechanism. The evolution only looks for the best solution. Compared with GA, all the particles tend to converge to the best solution quickly even in the local version in most cases. PSO parameter control From the above case, we can learn that there are two key steps when applying PSO to optimization problems: the representation of the solution and the fitness function. One of the advantages of PSO is that PSO take real numbers as particles. It is not like GA, which needs to change to binary encoding, or special genetic operators have to be used. For example, we try to find the solution for f(x) = x1^2 + x2^2+x3^2, the particle can be set as (x1, x2, x3), and fitness function is f(x). Then we can use the standard procedure to find the optimum. The searching is a repeat process, and the stop criteria are that the maximum iteration number is reached or the minimum error condition is satisfied. There are not many parameter need to be tuned in PSO. Here is a list of the parameters and their typical values. The number of particles: the typical range is 20 - 40. Actually for most of the problems 10 particles is large enough to get good results. For some difficult or special problems, one can try 100 or 200 particles as well. Dimension of particles: It is determined by the problem to be optimized, Range of particles: It is also determined by the problem to be optimized, you can specify different ranges for different dimension of particles. Vmax: it determines the maximum change one particle can take during one iteration. Usually we set the range of the particle as the Vmax for example, the particle (x1, x2, x3) X1 belongs [-10, 10], then Vmax = 20 Learning factors: c1 and c2 usually equal to 2. However, other settings were also used in different papers. But usually c1 equals to c2 and ranges from [0, 4] The stop condition: the maximum number of iterations the PSO execute and the minimum error requirement. for example, for ANN training in previous section, we can set the


minimum error requirement is one mis-classified pattern. the maximum number of iterations is set to 2000. This stop condition depends on the problem to be optimized. Global version vs. local version: we introduced two versions of PSO. Global and local version. Global version is faster but might converge to local optimum for some problems. Local version is a little bit slower but not easy to be trapped into local optimim. One can use global version to get quick result and use local version to refine the search. Another factor is inertia weight, which is introduced by Shi and Eberhart

iv. Stochastic Diffusion Search

Stochastic diffusion search (SDS) is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterized by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary, resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in some species of ant. A positive feedback mechanism ensures that, over time, a population of agents stabilize around the global-best solution. SDS is both an efficient and robust search and optimization algorithm, which has been extensively mathematically described.

Or in simple words we can say that It belongs to a family of swarm intelligence and naturally inspired search and optimisation algorithms which includes ant colony optimization, particle swarm optimization and genetic algorithms. It is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterized by the agent's current hypothesis.

v. Gravitational Search Algorithm

Gravitational search algorithm (GSA) is constructed based on the law of Gravity and the notion of mass interactions. The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses. In GSA, we have an isolated system of masses. Using the gravitational force, every mass in the system can see the situation of other masses. The gravitational force is therefore a way of transferring information between different masses.


vi. Intelligent Water Drops

Intelligent Water Drops algorithm (IWD) is a swarm-based nature-inspired optimization algorithm, which has been inspired from natural rivers and how they find almost optimal paths to their destination. These near optimal or optimal paths follow from actions and reactions occurring among the water drops and the water drops with their riverbeds. In the IWD algorithm, several artificial water drops cooperate to change their environment in such a way that the optimal path is revealed as the one with the lowest soil on its links. The solutions are incrementally constructed by the IWD algorithm. Consequently, the IWD algorithm is generally a constructive population-based optimization algorithm.

vii. Charged System Search

Charged System Search (CSS) is a new optimization algorithm based on some principles from physics and mechanics. CSS utilizes the governing laws of Coulomb and Gauss from electrostatics and the Newtonian laws of mechanics. CSS is a multi-agent approach in which each agent is a Charged Particle (CP). CPs can affect each other based on their fitness values and their separation distances. The quantity of the resultant force is determined by using the electrostatics laws and the quality of the movement is determined using Newtonian mechanics laws. CSS is applicable to all optimization fields; especially it is suitable for non-smooth or non-convex domains. This algorithm provides a good balance between the exploration and the exploitation paradigms of the algorithm which can considerably improve the efficiency of the algorithm and therefore the CSS also can be considered as a good global and local optimizer simultaneously.


V. APPLICATIONS OF SWARM TECHNOLOGY Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self assembly and interferometer. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors! Here are some of the applications of Swarm Technology.

a. Crowd Simulation Artists are using swarm technology as a means of creating complex interactive systems

or simulating crowds. Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system. Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats. The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple. Airlines have used swarm theory to simulate passengers boarding a plane. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.

Figure iii Crowd Simulation in Maya


b. Ant-Based Routing

The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based Routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of variations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then you pay for the cinema before you know how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence.

Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a software program uses swarm theory, or swarm intelligence -- the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Dr. Douglas A. Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive and depart quickly. The program can even alert a pilot of plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," Dr. Lawson says.

Figure iv Swarm Technology used in Arirlines


c. Clustering Behavior Of Ants

Ants build cemeteries by collecting dead bodies into a single place in the nest. They also organize the spatial disposition of larvae into clusters with the younger, smaller larvae in the cluster center and the older ones at its periphery. This clustering behavior has motivated a number of scientific studies.

Figure v Clustering behabour of Ants

d. Nest Building Behavior Of Wasps And Termites

Wasps build nests with a highly complex internal structure that is well beyond the

ognitive capabilities of a single wasp. Termites build nests whose dimensions are normous when compared to a single individual, which can measure as little as a few millimeters. Scientists have been studying the coordination mechanisms that allow the construction of these structures and have proposed probabilistic models exploiting insects behavior. Some of these models are implemented in computer programs to produce simulated structures that recall the morphology of the real nests.

Figure vi Nest Building Behavior Of Wasps And Termites


e. Flocking And Schooling In Birds And Fish

Scientists have shown that these elegant swarm-level behaviors can be understood as the result of a self-organized process where no leader is in charge and each individual bases its movement decisions solely on locally available information: the distance, perceived speed, and direction of movement of neighbours. These studies have inspired a number of computer simulations that are now used in the computer graphics industry for the realistic reproduction of flocking in movies and computer games.

Figure vii Flocking of birds

Figure viii Flocking simulation

Swarm Technology by Abhilash Nayak|

f. Ant Colony Optimization

In ant colony optimization (ACO), a set of

for good solutions to a given optimization problemthe minimum cost path on a weighted graph. The artificial antssolutions by moving on the graph. The solution construction process is stochasticbiased by a pheromone model, that is, a set ofcomponents the values of which are modified at runtime by the ants.


Ant Colony Optimization

In ant colony optimization (ACO), a set of software agents called "artificial ants" search good solutions to a given optimization problem transformed into the problem of finding

path on a weighted graph. The artificial ants incrementally build graph. The solution construction process is stochastic

biased by a pheromone model, that is, a set of parameters associated with graphof which are modified at runtime by the ants.

Figure ix ACO

software agents called "artificial ants" search transformed into the problem of finding

incrementally build graph. The solution construction process is stochastic and is

parameters associated with graph


g. Particle Swarm Optimization

It is inspired by social behaviors in flocks of birds and schools of fish. In practice, in the initialization phase each particle is given a random initial position and an initial velocity. The position of the particle represents a solution of the problem and has therefore a value, given by the objective function. At each iteration of the algorithm, each particle moves with a velocity that is a weighted sum of three components: the old velocity, a velocity component that drives the particle towards the location in the search space where it previously found the best solution so far, and a velocity component that drives the particle towards the location in the search space where the neighbor particles found the best solution so far.

h. Swarm Based Network Management Schoonderwoerd et al. proposed Ant-based Control (ABC), an algorithm for routing and

load balancing in circuit-switched networks; Di Caro and Dorigo proposed AntNet, an algorithm for routing in packet-switched networks. While ABC was a proofof- concept, AntNet, which is an ACO algorithm, was compared to many state-of-the-art algorithms and its performance was found to be competitive especially in situation of highly dynamic and stochastic data traffic as can be observed in Internet-like networks. An extension of AntNet has been successfully applied to ad-hoc networks.


i. Cooperative Behaviour In Swarms Of Robots

There are a number of swarm behaviours observed in natural systems that have inspired innovative ways of solving problems by using swarms of robots. This is what is called swarm robotics. In other words, swarm robotics is the application of swarm intelligence principles to the control of swarms of robots. As with swarm intelligence systems in general, swarm robotics systems can have either a scientific or an engineering flavour. Clustering in a swarm of robots was mentioned above as an example of artificial/scientific system.


VI. ADVANTAGES OF SWARM TECHNOLOGY ü It is easily adoptable as conventional workgroups devise various standard

operating procedures to react to predetermined stimuli. But swarms have better ability to adjust to new situations or to change beyond a narrow range of options.

ü Evolution is the result of adaptation. Conventional bureaucratic systems can shift the locus of adaptation (slowly) from one part of the system to another. In swarm systems, individual variation and imperfection lead to perpetual novelty, which leads to evolution

ü Resilient. A swarm is a collective system made up of multitudes in parallel, which results in enormous redundancy. Because the swarm is highly adaptable and evolves quickly, failures tend to be minimal.

VII. DISADVANTAGES OF SWARM TECHNOLOGY

ü It is non-optimal and uncontrollable as it is very difficult to exercise control over a swarm. Swarm systems require guidance in the way that a shepherd drives a herd by applying force at crucial leverage points.

ü It is unpredictable as the complexity of a swarm system leads to unforeseeable results. Emergent novelty is a primary characteristic of self-organisation by adaptive systems.

ü Non-understandable – Sequential systems are understandable; complex adaptive systems, instead, are a jumble of intersecting logic. Instead of A causing B, which in turn causes C, A indirectly causes everything, and everything indirectly causes A

ü It is non-immediate as linear systems tend to be very direct. Flip a switch and the light comes on. Simple collective systems tend to operate simply. But complex swarm systems with rich hierarchies take time.


VIII. CONCLUSION The idea of swarm behavior may still seem strange because we are used to relatively

linear bureaucratic models. In fact, this kind of behaviour characterizes natural systems ranging from flocks of birds to schools of fish. Humans are more complex than ants or fish and have lots more capacity for novel behavior, some unexpected results are likely, and for this reason, leading scientists and organizations will further pursue swarm approaches. Swarm Intelligence provides a distributive approach to the problem solving mimicking the very simple natural process of cooperation. According to my survey many solutions that had been previously solved using other AI approach like genetic algorithm neural network are also solve able by this approach also. Due to its simple architecture and adaptive nature like ACO has it is more likely to be seen much more in the future.

Documents

54426609 Swarm Inteligence Seminar Report