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Modeling And Simulation Of Foraging Behavior Of Animal Groups Based On Particle Swarm Model

Posted on:2010-03-04Degree:MasterType:Thesis
Country:ChinaCandidate:X MengFull Text:PDF
GTID:2208360278976204Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
Particle swarm optimization (PSO) is a stochastic global optimization algorithm through the cooperation and competition between particles based on the theory of swarm intelligence. PSO algorithm and other evolutionary algorithms have many similarities. They all use the concept of "swarm", indicated the gather of some individuals in a group of solution space, which is similar to group-foraging problem of the behavior ecology research. Some researchers abroad began to apply PSO algorithm to simulate the foraging behaviors of animal swarm, but the research is at beginning. There still are many problems, for example, the group-foraging environment is too simplistic and abstract; individual perception of group-foraging and the mode of animal information exchanging are single; and the animal foraging demand is certain, etc.. Because of these aforementioned reasons, the studies on foraging behavior of animals don't accord with the actual world.This thesis, on the basis of previous studies, applies Particle Swarm Optimization to set up more accurate model of group-foraging behavior and simulate the model. First of all, the group-foraging environment with random distribution of food is built. Then two methods of animal information exchanging are given,i.e. the overall interaction and the local interaction. From the aspect of perception of animals'looking for food, the sense of smell perception and fusion of the perception of smell and vision are studied. On this basis, four kinds of animal-foraging behavior under different patterns of information exchanging and perceptual manner are simulated. The simulation results show that: the promoted animal foraging model can simulate the foraging behavior of animal groups more actual. The study has got reference value and theoretical significance towards the understanding of animal group foraging behavior, as well as obtaining the global optimization intelligent algorithm based on animal group foraging behaviors.
Keywords/Search Tags:PSO algorithm, Group-foraging, Perception model, Animal group behavior
PDF Full Text Request
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