Swarm intelligence ( SI) is the of, systems, natural or artificial. The concept is employed in work on. The expression was introduced by and Jing Wang in 1989, in the context of cellular robotic systems.
Swarm Robotics Research Team. A Robotic Application of the Ant Colony Optimization Algorithm. Simulate using MATLAB; Devise a plan to simulate with robots; Develop software and troubleshoot; Run multiple tests and collect data.
SI systems consist typically of a population of simple or 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 of 'intelligent' global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include, bird, animal, fish. The application of swarm principles to is called, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.
Main article: Boids is an program, developed by in 1986, which simulates the behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the conference. The name 'boid' corresponds to a shortened version of 'bird-oid object', which refers to a bird-like object. As with most artificial life simulations, Boids is an example of behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:. separation: to avoid crowding local flockmates.
alignment: steer towards the average heading of local flockmates. cohesion: steer to move toward the average position (center of mass) of local flockmates More complex rules can be added, such as obstacle avoidance and goal seeking. Self-propelled particles (Vicsek et al. Main article: Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by et al. As a special case of the model introduced in 1986. 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 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. Metaheuristics. Main article: First published in 1989 Stochastic diffusion search (SDS) was the first Swarm Intelligence metaheuristic. SDS is an agent-based 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 communication used in ACO, in SDS agents communicate via a one-to-one communication strategy analogous to the procedure observed in.
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 global search and optimisation algorithm, which has been extensively mathematically described.
Recent work has involved merging the global search properties of SDS with other swarm intelligence algorithms. Ant colony optimization (Dorigo 1992). Main article: Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of modeled on the actions of an. ACO is a useful in problems that deal with finding better paths through graphs. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a representing all possible solutions.
Natural ants lay down 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 for better solutions. Particle swarm optimization (Kennedy, Eberhart & Shi 1995). Main article: Particle swarm optimization (PSO) is a 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, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some 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 is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of. Applications Swarm Intelligence-based techniques can be used in a number of applications. Military is investigating swarm techniques for controlling unmanned vehicles. The is thinking about an orbital swarm for self-assembly and interferometry.
Is investigating the use of swarm technology for planetary mapping. A 1992 paper by and 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 to help locate tumours. Swarm intelligence has also been applied for. Ant-based routing The use of swarm intelligence in has also been researched, in the form of.
This was pioneered separately by Dorigo et al. And in the mid-1990s, with a number of variations since.
Basically, this uses a 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 one would pay for the cinema before one knows 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.
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A very different-ant inspired swarm 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 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,' 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. Crowd simulation Artists are using swarm technology as a means of creating complex interactive systems. 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 also made use of swarm technology for showing the movements of a group of bats.
Made use of similar technology, known as, 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). Human swarming Enabled by mediating software such as the SWARM platform (formally unu) from, networks of distributed users can be organized into 'human swarms' through the implementation of real-time closed-loop control systems. As published by (2015), such real-time systems enable groups of human participants to behave as a unified that works as a single entity to make predictions, answer questions, and evoke opinions. Such systems, also referred to as 'Artificial Swarm Intelligence' (or the brand name Swarm AI) have been shown to significantly amplify human intelligence, resulting in a string of high-profile predictions of extreme accuracy. Academic testing shows that human swarms can out-predict individuals across a variety of real-world projections.
Famously, human swarming was used to correctly predict the Kentucky Derby Superfecta, against 541 to 1 odds, in response to a challenge from reporters. Swarm grammars Swarm grammars are swarms of that can be evolved to describe complex properties such as found in art and architecture. These grammars interact as agents behaving according to rules of swarm intelligence. Such behavior can also suggest algorithms, in particular when mapping of such swarms to neural circuits is considered. Swarmic art 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 (, SDS) and the other algorithm mimicking the behaviour of birds flocking (, PSO)—to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting 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 analysed under the philosophical light of the 'rhizome' in the context of 's 'Orchid and Wasp' metaphor. In a more recent work of al-Rifaie et al., 'Swarmic Sketches and Attention Mechanism', introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of PSO is used to produce a 'swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles—attention to areas with more details—associated to them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings.
In other works while PSO is responsible for the sketching process, SDS controls the attention of the swarm. In a similar work, 'Swarmic Paintings and Colour Attention', non-photorealistic images are produced using SDS algorithm which, in the context of this work, is responsible for colour attention.
The 'computational creativity' of the above-mentioned systems are discussed in through the two prerequisites of creativity (i.e. Freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation. Michael Theodore and use swarm intelligent art installation to explore what it takes to have engineered systems to appear lifelike. Notable researchers.