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The Study Of Particle Swarm Optimization

Posted on:2005-04-28Degree:MasterType:Thesis
Country:ChinaCandidate:J Y LiFull Text:PDF
GTID:2168360122971459Subject:Chemical Engineering
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Particle swarm optimization (PSO) is an evolutionary computation technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Similar to Genetic Algorithms (GA), PSO is a population based optimization tool. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, are "flown" through the problem space by following the current optimum particles.First, this thesis reviewed the PSO and its research achievement. Base to the Simple Particle Optimization (SPSO), the paper discussed the effect of adaptive inertia weight and presented some kinds of improved PSO, such as APSO, HPSO, CPSO, etc.At the same time, the thesis proposed an asynchronous pattern from analyzing on the biologic character of particle swarm optimization, as to the SPSO is always converges very quickly towards the optimal positions but may slow its convergence speed when it is near a minimum. The asynchronous pattern is programmed with Java multiple threads, and considered each of the particles as a thread.Finally, a composite PSO (CPSO) using simple genetic algorithm (SGA) to optimize the control parameters is developed, in order to overcome the disadvantage of choosing heuristic parameter for PSO. The CPSO has been applied successfully to the nonlinear parameter estimation of heavy oil thermal cracking model.
Keywords/Search Tags:particle swarm optimization, swarm intelligence, evolutionary computation technique, asynchronous, composite particle swarm optimization, Java multiple threads
PDF Full Text Request
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