An Adaptive Particle Swarm Optimization Algorithm Based on Cloud Model
2010 ◽
Vol 129-131
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pp. 612-616
Keyword(s):
In this paper, an adaptive particle swarm optimization algorithm based on cloud model (C-APSO) is proposed. In the suggested method, the velocities of the all particles are adjusted based on the strategy that a particle whose fitness value is nearer to the optimal particle will fly with smaller velocity. Considering the properties of randomness and stable tendency of a normal cloud model, a Y-conditional normal cloud generator is used to gain the inertial factors of the particles. The simulations of function optimization show that the proposed method has advantage of global convergence property and can effectively alleviate the problem of premature convergence.
2009 ◽
Vol 28
(12)
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pp. 3058-3061
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2013 ◽
Vol 760-762
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pp. 2194-2198
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2013 ◽
Vol 427-429
◽
pp. 1934-1938
2012 ◽
Vol 7
(10)
◽
pp. 166-174
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