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| A Novel Hybrid Particle Swarm Optimization Algorithm |
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Abstract: The basic Particle Swarm Optimization (bPSO) algorithm suffers from some defects, such as the tendency to converge into a
local extremum, the slow convergence rate and the low convergence accuracy in the late stage of evolution. A new algorithm HPSO based on hybrid PSO-GA(Particle Swarm Optimization and Genetic Algorithm) is proposed in this paper. The normal mutation operator is introduced into the basic particle swarm optimization algorithm. By taking advantage of the searching abilities of these two methods, the population diversity is enhanced; the global search ability and search efficiency are improved. The new HPSO is used in several typical function optimizations, and it is shown that the proposed method, while retaining the advantages of bPSO, such as the ease to realize and operate and high speed in calculation, with the introduction of the normal mutation operator, greatly improves the search ability and search efficiency in the late stage of evolution. The new Hybrid algorithm enjoys higher optimization capability with less particles and less generations than bPSO, GA and CPSO.
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Received: 01 June 2010
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Corresponding Authors:
qian peng fei
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2010, Vol. 28 
