A Modified Particle Swarm Optimization Algorithm

Jie He, Hui Guo

Abstract


In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Damping) approach to make fly off the search area of the particles of size remain unchanged and avoid the local optimal solution, with three input XOR problem to testing the improvement of the particle swarm optimization algorithm and the results showed that the improved algorithm not only increase global optimization ability, but also avoid the prematurity, convergence problem.

 

DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.2947 


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