Swarm intelligence
Encyclopedia
Swarm intelligence 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 inspiration often comes from nature, especially biological systems. 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 robot
s 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.
in a swarm of entities can, over time, evolve and result in more effective swarm behaviour.
(ACO) is a class of optimization
algorithm
s 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 pheromone
s 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
.
of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically-inspired computing, and natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
(CS) mimics the brooding behaviour of some cuckoo species, which use host
birds to hatch their eggs and raise their chicks.
This cuckoo search algorithm is enhanced with Levy flights
with jump steps drawn from Levy distribution.
Recent studies suggested that CS can outperform other algorithms
such as particle swarm optimization
. For example, a comparison of the cuckoo search with PSO, DE and ABC suggest that CS and DE algorithms provide more robust results than PSO and ABC.
(FA) is another swarm-based algorithm, which was inspired by the
flashing behaviour of fireflies. Light intensity is associated with attractiveness
of a firefly
, and such attraction enable the fireflies with the ability
to subdivide into small groups and each subgroup swarm around the local modes.
Therefore, firefly algorithm is specially suitable for multimodal optimization problems.
In fact, FA has been applied in continuous optimization, traveling salesman problem,
clustering, image processing
and feature selection.
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 (Rashedi, Nezamabadi-pour and Saryazdi 2009).
In GSA, agents are considered as objects and their performance is measured by their masses. All these objects attract each other by a gravity force, and this force causes a movement of all objects globally towards the objects with heavier masses. The heavy masses correspond to good solutions of the problem. The position of the agent corresponds to a solution of the problem, and its mass is determined using a fitness function. By lapse of time, masses are attracted by the heaviest mass. We hope that this mass would present an optimum solution in the search space. The GSA could be considered as an isolated system of masses. It is like a small artificial world of masses obeying the Newtonian laws of gravitation and motion (Rashedi, Nezamabadi-pour and Saryazdi 2009). A multi-objective variant of GSA, called Non-dominated Sorting Gravitational Search Algorithm (NSGSA), was proposed by Nobahari and Nikusokhan in 2011.
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.
(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 timestep. 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.
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 specially 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.
(SPP), also referred to as the Couzin–Vicsek algorithm, was introduced in 1995 by Vicsek and Couzin et al. as a special case of the boids
model introduced in 1986 by Reynolds
. A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood. SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.
(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 parameterised 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 stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation algorithm, which has been extensively mathematically described.
is thinking about an orbital swarm for self assembly and interferometry. 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. Swarm intelligence has also been applied for data mining.
.
Decentralization
__FORCETOC__Decentralization or decentralisation is the process of dispersing decision-making governance closer to the people and/or citizens. It includes the dispersal of administration or governance in sectors or areas like engineering, management science, political science, political economy,...
, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
. The expression was introduced by Gerardo Beni
Gerardo Beni
Gerardo Beni is a professor of electrical engineering at University of California, Riverside who, with Jin Wang, is known as the originator of the term 'swarm intelligence' in the context of cellular robotics and the concept of 'electrowetting' , with Susan Hackwood...
and Jing Wang in 1989, in the context of cellular robotic
Cellular automaton
A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. It consists of a regular grid of cells, each in one of a finite number of states, such as "On" and "Off"...
systems.
SI systems are typically made up of a population of simple agents
Intelligent agent
In artificial intelligence, an intelligent agent is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals . Intelligent agents may also learn or use knowledge to achieve their goals...
or boids
Boids
Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference...
interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. 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
Emergence
In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems....
of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies
Ant colony
An ant colony is an underground lair where ants live, eat and mate. Colonies consist of a series of underground chambers, connected to each other and the surface of the earth by small tunnels. There are rooms for nurseries, food storage, and mating...
, bird flocking
Flocking (behavior)
Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, the swarming behavior of insects, and herd behavior of land animals....
, animal herding
Herding
Herding is the act of bringing individual animals together into a group , maintaining the group and moving the group from place to place—or any combination of those. While the layperson uses the term "herding", most individuals involved in the process term it mustering, "working stock" or...
, bacterial growth, and fish schooling
Shoaling and schooling
In biology, any group of fish that stay together for social reasons are said to be shoaling , and if, in addition, the group is swimming in the same direction in a coordinated manner, they are said to be schooling . In common usage, the terms are sometimes used rather loosely...
.
The application of swarm principles to robot
Robot
A robot is a mechanical or virtual intelligent agent that can perform tasks automatically or with guidance, typically by remote control. In practice a robot is usually an electro-mechanical machine that is guided by computer and electronic programming. Robots can be autonomous, semi-autonomous or...
s is called swarm robotics
Swarm robotics
Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment...
, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.
Altruism algorithm
Researchers in Switzerland have developed an algorithm based on Hamilton's rule of kin selection. The algorithm shows how altruismAltruism in animals
Altruism is a well-documented animal behaviour, which appears most obviously in kin relationships but may also be evident amongst wider social groups, in which an animal sacrifices its own well-being for the benefit of another animal.- Overview :...
in a swarm of entities can, over time, evolve and result in more effective swarm behaviour.
Ant colony optimization
Ant colony optimizationAnt colony optimization
In computer science and operations research, the ant colony optimization algorithm ' is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs....
(ACO) is a class of optimization
Optimization (mathematics)
In mathematics, computational science, or management science, mathematical optimization refers to the selection of a best element from some set of available alternatives....
algorithm
Algorithm
In mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Algorithms are used for calculation, data processing, and automated reasoning...
s modeled on the actions of an ant colony
Ant colony
An ant colony is an underground lair where ants live, eat and mate. Colonies consist of a series of underground chambers, connected to each other and the surface of the earth by small tunnels. There are rooms for nurseries, food storage, and mating...
. 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
Parameter space
In science, a parameter space is the set of values of parameters encountered in a particular mathematical model. Often the parameters are inputs of a function, in which case the technical term for the parameter space is domain of a function....
representing all possible solutions. Real ants lay down pheromone
Pheromone
A pheromone is a secreted or excreted chemical factor that triggers a social response in members of the same species. Pheromones are chemicals capable of acting outside the body of the secreting individual to impact the behavior of the receiving individual...
s 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
Bees algorithm
In computer science and operations research, the bees algorithm is a population-based search algorithm first developed in 2005. It mimics the food foraging behaviour of swarms of honey bees...
, which is more analogous to the foraging patterns of the honey bee
Honey bee
Honey bees are a subset of bees in the genus Apis, primarily distinguished by the production and storage of honey and the construction of perennial, colonial nests out of wax. Honey bees are the only extant members of the tribe Apini, all in the genus Apis...
.
Artificial bee colony algorithm
Artificial Bee Colony (ABC) algorithm is a swarm based meta-heuristic algorithm introduced by Karaboga in 2005 , and simulates the foraging behaviour of honey bees. The ABC algorithm has three phases: employed bee, onlooker bee and scout bee. In the employed bee and the onlooker bee phases, bees exploit the sources by local searches in the neighbourhood of the solutions selected based on deterministic selection in the employed bee phase and the probabilistic selection in the onlooker bee phase. In the scout bee phase which is an analogy of abandoning exhausted food sources in the foraging process, solutions that are not beneficial anymore for search progress are abandoned, and new solutions are inserted instead of them to explore new regions in the search space. The algorithm has a good-balanced exploration and exploitation ability.Artificial immune systems
Artificial immune systems (AIS) concerns the usage of abstract structure and functionof the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically-inspired computing, and natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
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.Cuckoo search
Cuckoo searchCuckoo search
Cuckoo search is an optimization algorithm developed by Xin-she Yang and Suash Debin 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds . Some host birds can engage direct conflict with the intruding cuckoos...
(CS) mimics the brooding behaviour of some cuckoo species, which use host
birds to hatch their eggs and raise their chicks.
This cuckoo search algorithm is enhanced with Levy flights
with jump steps drawn from Levy distribution.
Recent studies suggested that CS can outperform other algorithms
such as particle swarm optimization
Particle swarm optimization
In computer science, particle swarm optimization is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality...
. For example, a comparison of the cuckoo search with PSO, DE and ABC suggest that CS and DE algorithms provide more robust results than PSO and ABC.
Firefly Algorithm
Firefly algorithmFirefly algorithm
The firefly algorithm is a metaheuristic algorithm, inspired by the flashing behaviour of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies...
(FA) is another swarm-based algorithm, which was inspired by the
flashing behaviour of fireflies. Light intensity is associated with attractiveness
of a firefly
Firefly
Lampyridae is a family of insects in the beetle order Coleoptera. They are winged beetles, and commonly called fireflies or lightning bugs for their conspicuous crepuscular use of bioluminescence to attract mates or prey. Fireflies produce a "cold light", with no infrared or ultraviolet frequencies...
, and such attraction enable the fireflies with the ability
to subdivide into small groups and each subgroup swarm around the local modes.
Therefore, firefly algorithm is specially suitable for multimodal optimization problems.
In fact, FA has been applied in continuous optimization, traveling salesman problem,
clustering, image processing
Image processing
In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image...
and feature selection.
Gravitational search algorithm
Gravitational search algorithm (GSA) is constructed based on the law of gravityLaw of Gravity
"Law of Gravity" is the fifteenth episode of the seventh season of the television series CSI: Crime Scene Investigation.-Plot:Before the start of his shift, Keppler eats dinner at a diner where he runs into an older man named Frank McCarty...
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 (Rashedi, Nezamabadi-pour and Saryazdi 2009).
In GSA, agents are considered as objects and their performance is measured by their masses. All these objects attract each other by a gravity force, and this force causes a movement of all objects globally towards the objects with heavier masses. The heavy masses correspond to good solutions of the problem. The position of the agent corresponds to a solution of the problem, and its mass is determined using a fitness function. By lapse of time, masses are attracted by the heaviest mass. We hope that this mass would present an optimum solution in the search space. The GSA could be considered as an isolated system of masses. It is like a small artificial world of masses obeying the Newtonian laws of gravitation and motion (Rashedi, Nezamabadi-pour and Saryazdi 2009). A multi-objective variant of GSA, called Non-dominated Sorting Gravitational Search Algorithm (NSGSA), was proposed by Nobahari and Nikusokhan in 2011.
Intelligent water drops
Intelligent Water Drops algorithm (IWD) is a swarm-based nature-inspired optimization algorithm, which has been inspired by natural rivers and how they find almost optimal paths to their destination. These near optimal or optimal paths follow from actions andreactions 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.
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
In computer science, particle swarm optimization is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality...
(PSO) is a global optimization
Global optimization
Global optimization is a branch of applied mathematics and numerical analysis that deals with the optimization of a function or a set of functions to some criteria.- General :The most common form is the minimization of one real-valued function...
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
Velocity
In physics, velocity is speed in a given direction. Speed describes only how fast an object is moving, whereas velocity gives both the speed and direction of the object's motion. To have a constant velocity, an object must have a constant speed and motion in a constant direction. Constant ...
, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness
Fitness (biology)
Fitness is a central idea in evolutionary theory. It can be defined either with respect to a genotype or to a phenotype in a given environment...
criterion after each timestep. 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
Simulated annealing
Simulated annealing is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete...
is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
River formation dynamics
River formation dynamics (RFD) is an heuristic methodsimilar 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 specially 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.
Self-propelled particles
Self-propelled particlesSelf-propelled particles
Self-propelled particles , also referred to as self-driven particles or as the Couzin–Vicsek algorithm, is a concept used to model swarm behaviour. The concept was introduced in 1995 by Vicsek and Couzin et al. as a special case of the Boids model introduced in 1986 by Reynolds...
(SPP), also referred to as the Couzin–Vicsek algorithm, was introduced in 1995 by Vicsek and Couzin et al. as a special case of the boids
Boids
Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference...
model introduced in 1986 by Reynolds
Craig Reynolds (computer graphics)
Craig W. Reynolds , is an artificial life and computer graphics expert, who created the Boids artificial life simulation in 1986. Reynolds worked on the film Tron as a scene programmer, and on Batman Returns as part of the video image crew. He is the author of the OpenSteer library.-External...
. A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood. SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.
Stochastic diffusion search
Stochastic diffusion searchStochastic Diffusion Search
Stochastic diffusion search was first described in 1989 as a population-based, pattern-matching algorithm [Bishop, 1989]. It belongs to a family of swarm intelligence and naturally inspired search and optimisation algorithms which includes ant colony optimization, particle swarm optimization and...
(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 parameterised 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
Stigmergy
Stigmergy is a mechanism of indirect coordination between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent...
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 stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation algorithm, which has been extensively mathematically described.
Applications
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 AgencyEuropean Space Agency
The European Space Agency , established in 1975, is an intergovernmental organisation dedicated to the exploration of space, currently with 18 member states...
is thinking about an orbital swarm for self assembly and interferometry. NASA
NASA
The National Aeronautics and Space Administration is the agency of the United States government that is responsible for the nation's civilian space program and for aeronautics and aerospace research...
is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis
M. Anthony Lewis (roboticist)
M. Anthony Lewis, Ph.D., is a robotics researcher and CEO of Iguana Robotics, a company specializing in the development of biomorphic robotics technologies....
and George A. Bekey
George A. Bekey
George A. Bekey is a renowned American roboticist and the Professor Emeritus of Computer Science, Electrical Engineering and Biomedical Engineering at the University of Southern California. He is a member of the National Academy of Engineering and a Fellow in various professional societies...
discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Swarm intelligence has also been applied for data mining.
Crowd simulation
Artists are using swarm technology as a means of creating complex interactive systems or simulating crowdsCrowd simulation
Crowd simulation is the process of simulating the movement of a large number of objects or characters, now often appearing in 3D computer graphics for film...
.