From Wikipedia, the free encyclopedia
Swarm intelligence (SI) 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 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.
Researchers in Switzerland have developed an algorithm based on Hamilton’s rule of kin selection. The algorithm shows how altruism in a swarm of entities can, over time, evolve and result in more effective swarm behaviour.
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. Natural 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.
Artificial bee colony algorithm
Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005, and simulates the foraging behaviour of honeybees. 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 well-balanced exploration and exploitation ability.
Artificial cooperative search algorithm
Artificial cooperative search algorithm (ACS) is a bi-population based swarm intelligence algorithm introduced by Civicioglu in 2013, which analogically simulates the predator-prey interaction. The success of ACS algorithm in solving numerical optimization problems is compared to the problem solving successes of Particle Swarm Optimization algorithm (PSO), Strategy Adaptation Based Differential Evolution algorithm (SADE), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Biogeography-Based Optimization (BBO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Cuckoo-Search algorithm (CK), and Differential Search Algorithm (DSA).
Artificial immune systems
Artificial immune systems (AIS) concerns the usage of abstract structure and function 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.
Bat algorithm (BA) inspired by the echolocation behavior of microbats. BA uses a frequency-tuning and automatic balance of exploration and exploitation by controlling loudness and pulse emission rates.
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 (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 Lévy flights with jump steps drawn from Lévy 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.
Differential search algorithm
Differential search algorithm (DSA) has been inspired by migration of superorganisms. The problem solving success of DSA was compared to the successes of ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011 and CMA-ES algorithms for solution of numerical optimization problems in 2012. Matlab code-link has been provided in Civicioglu, P.,(2012).
Firefly algorithm (FA) 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 especially suitable for multimodal optimization problems. In fact, FA has been applied in continuous optimization, traveling salesman problem, clustering, image processing and feature selection.
Glowworm swarm optimization
Glowworm swarm optimization (GSO), introduced by Krishnanand and Ghose in 2005 for simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function.
Gravitational search algorithm
Gravitational search algorithm (GSA) 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, there is 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) inspired by 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.
Krill herd algorithm
Krill herd (KH) is a novel biologically inspired algorithm proposed by Gandomi and Alavi in 2012. The KH algorithm is based on simulating the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors:
1. movement induced by the presence of other individuals;
2. foraging activity; and
3. random diffusion.
The derivative information is not necessary in the KH algorithm because it uses a stochastic random search instead of a gradient search. For each metaheuristic algorithm, it is important to tune its related parameters. One of interesting parts of the proposed algorithm is that it carefully simulates the krill behavior and it uses the real world empirical studies to obtain the coefficients. Because of this fact, only time interval should be fine-tuned in the KH algorithm. This can be considered as a remarkable advantage of the proposed algorithm in comparison with other nature-inspired algorithms. The validation phases indicate that the KH method is very encouraging for its future application to optimization tasks.
Magnetic optimization algorithm
Magnetic Optimization Algorithm (MOA), proposed by Tayarani in 2008, is an optimization algorithm inspired by the interaction among some magnetic particles with different masses. In this algorithm, the possible solutions are some particles with different masses and different magnetic fields. Based on the fitness of the particles, the mass and the magnetic field of each particle is determined, thus the better particles are more massive objects with stronger magnetic fields. The particles in the population apply attractive forces to each other and so move in the search space. Since the better solutions have greater mass and magnetic field, the inferior particles tend to move toward the fitter solutions and thus migrate to area around the better local optima, where they wander in search of better solutions.
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms. The multi-swarm framework is especially fitted for the optimization on multi-modal problems, where multiple (local) optima exist.
Particle swarm optimization
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 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.
River formation dynamics
River formation dynamics (RFD) is a heuristic method similar to ant colony optimization (ACO). It 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.
Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al as a special case of the boids model introduced in 1986 by Reynolds. A swarm is modeled 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 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 Leptothorax acervorum. 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 global search and optimization algorithm, which has been extensively mathematically described. Recent work has involved merging the global search properties of SDS with other swarm intelligence algorithms
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 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. Conversely al-Rifaie and Aber have used Stochastic Diffusion Search to help locate tumours. Swarm intelligence has also been applied for data mining.
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 (Rheingold: 2002, P175).
The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives. A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. A very different ant inspired swam intelligence algorithm, Stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances.
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,” Douglas A. Lawson explains. As a result, the “colony” of pilots always go to gates they can arrive at and depart from 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,” Lawson says.
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.(Miller, 2010, xii-xviii).
In a series of works al-Rifaie et al have successfully used two swarm intelligence algorithms – one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (Stochastic diffusion search (SDS)) and the other algorithm mimicking the behaviour of birds ﬂocking (Particle swarm optimization PSO) – to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the ‘birds ﬂocking’ – as they seek to follow the input sketch – and the global behaviour of the ‘ants foraging’ – as they seek to encourage the ﬂock to explore novel regions of the canvas. The ‘creativity’ of this hybrid swarm system has been analyzed under the philosophical light of the ‘rhizome’ in the context of Deleuze’s well-known ‘Orchid and Wasp’ metaphor.
In popular culture
Swarm intelligence-related concepts and references can be found throughout popular culture, frequently as some form of collective intelligence or group mind involving far more agents than used in current applications.
- Science fiction writer Olaf Stapledon may have been the first to discuss swarm intelligences equal or superior to humanity. In Last and First Men (1931), a swarm intelligence from Mars consists of tiny individual cells that communicate with each other by radio waves; in Star Maker (1937) swarm intelligences founded numerous civilizations.
- The Invincible (1964), a science fiction novel by Stanisław Lem where a human spaceship finds intelligent behavior in a flock of small particles that were able to defend themselves against what they found as a menace.
- In the dramatic novel and subsequent mini-series The Andromeda Strain (1969) by Michael Crichton, an extraterrestrial virus communicates between individual cells and displays the ability to think and react individually and as a whole, and as such displays a semblance of “swarm intelligence”.
- Ygramul, the Many – an intelligent being consisting of a swarm of many wasp-like insects, a character in the novel The Neverending Story (1979) written by Michael Ende. Ygramul is also mentioned in a scientific paper Flocks, Herds, and Schools written by Knut Hartmann (Computer Graphics and Interactive Systems, Otto-von-Guericke-University of Magdeburg).
- Swarm (1982), a short story by Bruce Sterling about a mission undertaken by a faction of humans, to understand and exploit a space-faring swarm intelligence.
- The Borg (1989) in Star Trek
- The Hacker and the Ants (1994), a book by Rudy Rucker on AI ants within a virtual environment.
- Hallucination (1995), a posthumous short story by Isaac Asimov about an alien insect-like swarm, capable of organization and provided with a sort of swarm intelligence.
- The Zerg (1998) of the Starcraft universe demonstrate such concepts when in groups and enhanced by the psychic control of taskmaster breeds.
- Wyrm (1998), a novel by Mark Fabi, deals with a virus developing emergent intelligence on the Internet.
- Decipher (2001) by Stel Pavlou deals with the swarm intelligence of nanobots that guard against intruders in Atlantis.
- In the video game series Halo, the Covenant (2001) species known as the Hunters are made up of thousands of worm-like creatures which are individually non-sentient, but, collectively form a sentient being.
- Prey (2002), by Michael Crichton deals with the danger of nanobots escaping from human control and developing a swarm intelligence.
- The science fiction novel The Swarm (2004), by Frank Schätzing, deals with underwater single-celled creatures who act in unison to destroy humanity.
- In the video game Mass Effect (2007), a galactic race known as the Quarians created a race of humanoid sentient machines known as the Geth.
- Cellular automaton
- Differential evolution
- Evolutionary computation
- Global brain
- Harmony search
- Promise theory
- Swarm Development Group
- The Wisdom of Crowds
- Wisdom of the crowd
TABACCO: Your initial response to any form of Socialism or Hive Intelligence will probably be Negative. We are first and foremost Creatures of Habit. And any threat to our Individual Space and Initiative will elicit extremely uncomfortable feelings.
But when you evaluate the current system: Capitalism, you must arrive at the same conclusion that I have reached – Capitalism only works well for the Few, the Elite, the Have-Mores! Capitalism is failing the Rest of Us!
No System can ever be “perfect” for one and for all. That is an unobtainable goal. If History has taught us nothing else, it has taught us this.
Have-More offspring are born with silver spoons in their mouths. Their talent, intellect and future contributions to man’s advancement are neither insured nor required.
Those of us, who are born without those silver spoons, are probably doomed to die without them too. A few raise themselves up by either ingenuity or blind luck. Capitalists are always more than willing to point out those Capitalists, who were born poor, and climbed the socio-economic ladder to economic success. But that is a distinct minority. It is comparable to buying a Super Lottery Ticket. Someone will eventually win it, but not many.
Where is it written that only those born with those Silver Spoons, those with the intellect, drive, good fortune or creativeness, should be the only ones to enjoy a life of happiness? I do not suggest that everyone should be a Donald Trump or even a homeowner. However, a System that creates such disparity between the Highest of the High and the Lowest of the Low must be wrong – except to the Greediest among us. Certainly Capitalism engenders Greed – particularly to those possessing those Silver Spoons. Gordon Gekko was NOT the hero of ‘Wall Street’, nor is Greed Good!
Tabacco: I consider myself both a funnel and a filter. I funnel information, not readily available on the Mass Media, which is ignored and/or suppressed. I filter out the irrelevancies and trivialities to save both the time and effort of my Readers and bring consternation to the enemies of Truth & Fairness! When you read Tabacco, if you don’t learn something NEW, I’ve wasted your time.
Tabacco is not a blogger, who thinks; I am a Thinker, who blogs. Speaking Truth to Power!
In 1981′s ‘Body Heat’, Kathleen Turner said, “Knowledge is power”.