19-06-2013, 11:36 AM
Swarm intelligence
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ABSTRACT:
Swarm intelligence (SI) describes the collective behavior 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 behavior 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 behavior in birds, while the terms shoaling or schooling are used to refer to swarming behavior in fish. The swarm size is a major parameter of a swarm.
INTRODUCTION:
SI is the property of a system whereby the collective behaviors 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 non-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.
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 neighbor
2. Remain close to your neighbors
3. Avoid collisions with your neighbors
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 neighbors to avoid collision. Slightly further away, in the zone of alignment, the focal animal will seek to align its direction of motion with its neighbors. In the outermost zone of attraction,
RIVER FORMATION DYNAMICS
This method is similar to that of the “Ant Colony Optimization”. In fact, this can be seen as a gradient version of Ant Colony Optimization, based on copying how water forms rivers by eroding the ground and depositing sediments. 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.
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.
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.
CONCLUSION
The idea of swarm behavior may still seem strange because we are used to relatively linear bureaucratic models. In fact, this kind of behavior 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.