Abstract: Standard Particle Swarm Optimization（SPSO） easily leads to premature convergence in optimizing path problem. To overcome this shortcoming and according to university geographic coordinates， a new hybrid PSO algorithm of inverted sequence variation is proposed for solving university path problem. To balance the ability of local search and global search of PSO and enhance the population diversity, variation condition is a self-balancing strategy. According to the reverse mutation rate operator, new groups start to do position variation for the particles to get rid of local minima and continue iterative update operation. The C+〖KG-*2〗+ programming of Visual Studio 2005 is used to make simulation. The results show that this algorithm can not only effectively solve the university path problem, but also is of high convergence precision and overcomes premature convergence effectively.
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Key words: university path, reverse mutation rate, inverted sequence variation, new hybrid PSO algorithm