Synopsis
Many algorithms based on the swarming behavior of a variety of creatures like honey bees ,ants, fishes, birds have been developed in the literature by the researchers in recent years as Particle Swarm Optimization(PSO)[1], Ant Colony Optimization (ACO)[2], Artificial Bee Colony (ABC)[3] and Differential Evolution (DE)[4] algorithms etc.Various popular examples of swarm intelligence come from the world of animals, such as fish schooling, bird flocking and bugs swarm. There are various algorithms based on metaheuristic derived from nature and their applications are used in problem solving. In the present paper a brief survey on a Swarm Intelligence based algorithm named Spider Monkey Optimization (SMO)[5] with a focus on its theory, developments and applications has been discussed. SMO is a stochastic technique for optimization inspired by the social behavior of spider monkeys that mimics the foraging behavior of spider monkeys.SMO technique is based on population repetitive methodology.SMO is a subclass of swarm intelligence, proposed by Jagdish Chand Bansal et al.[4].It is good at exploration and exploitation and also there is possibilities of further improvements .The social behavior of spider monkeys is an example of fission fusion system.
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Spider Monkey Optimization Algorithm with Theory and Developments
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 48 pages. 11.00x8.50x0.12 inches. In Stock. N° de réf. du vendeur zk1723893048
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