Download Particle Swarm Optimization Methods for Pattern Recognition by Omran M. G. H. PDF

Download Particle Swarm Optimization Methods for Pattern Recognition by Omran M. G. H. PDF

By Omran M. G. H.

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2003], Reynolds et al. [2003], Higashi and Iba [2003] and Esquivel and Coello Coello [2003]. 8 Improvements to PSO The improvements presented in this section are mainly trying to address the problem of premature convergence associated with the original PSO. These improvements usually try to solve this problem by increasing the diversity of solutions in the swarm. 34 Constriction Factor Clerc [1999] and Clerc and Kennedy [2001] proposed using a constriction factor to ensure convergence. The constriction factor can be used to choose values for w, c1 and c2 to ensure that the PSO converges.

6) For the gbest model, the best particle is determined from the entire swarm by selecting the best personal best position. 7) where s denotes the size of the swarm. The velocity update step is specified for each dimension j ∈ 1,…,Nd, hence, vi,j represents the jth element of the velocity vector of the ith particle. 8) where w is the inertia weight, c1 and c 2 are the acceleration constants, and r1, j (t ) , r2, j (t ) ~ U (0,1) . 8) consists of three components, namely • The inertia weight term, w, which was first introduced by Shi and Eberhart [A modified 1998].

Randomly initialize the K cluster centroids 2. 5) | z p ) w( z p ) until a stopping criterion is satisfied. In the above algorithm, u (mk | z p ) is the membership function which quantifies the membership of pattern zp to cluster k.

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