A modified particle swarm optimizer

Evolutionary artificial neural networks by multi-dimensional particle swarm optimization

“…Another reason for the premature convergence is that particles are flown through a single point which is (randomly) determined by gbest and pbest positions and this point is not even guaranteed to be a local optimum (Van den Bergh & Engelbrecht, 2002). Various modifications and PSO variants have been proposed in order to address this problem such as Abraham, Das, and Roy (2007), Christopher and Seppi (2004), Clerc (1999), Eberhart, Simpson, and Dobbins (1996), Higashi and Iba (2003), Kaewkamnerdpong and Bentley (2005), Lovberg (2002), Lovberg and Krink (2002), Peng, Reynolds, and Brewster (2003), Peram, Veeramachaneni, and Mohan (2003), Ratnaweera, Halgamuge, and Watson (2003), Ratnaweera, Halgamuge, and Watson (2002), Riget and Vesterstrom (2002), Richards and Ventura (2003), Shi and Eberhart (1998), Shi and Eberhart (2001), Van den Bergh and Engelbrecht (2002), Xie, Zhang, and Yang (2002a), Xie, Zhang, and Yang (2002b), Xie, Zhang, and Yang (2002c) and Zhang and Xie (2003).…”

“…where w is the inertia weight and c 1 , c 2 are the acceleration constants which are initially set to 2, (Shi & Eberhart, 1998). r 1,j ∼ U(0, 1) and r 2,j ∼ U(0, 1) are random variables with uniform distribution.…”

“…Recall from the earlier discussion that the first term in the summation is the memory term, which represents the role of the previous velocity over the current velocity, the second term is the cognitive component, which represents the particle's own experience and the third term is the social component through which the particle is ''guided'' by the gbest particle towards the GB solution so far obtained. Although the use of inertia weight, w,was later added by Shi and Eberhart (1998), into the velocity update equation, it is widely accepted as the basic form of PSO algorithm. A larger value of w favors exploration while a small inertia weight favors exploitation.…”

Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers

“…This dissertation compares the NFC-SPSO controller to the NFC-CPSO controller [11], the NFC-LPSO controller [12] and the NFC-PSO controller [5]. In the PSO, LPSO and CPSO, the cognitive coefficient 1  was set to 2, the society coefficient 2  was set to 2, and the population size was set to 200.…”

Searching for the Best Points of Interpolation Using Swarm Intelligence Techniques

“…et al 1996); ; (Kennedy, J. et al, 2001); ( Shi, Y.H. et al 2001); ( Shi, Y. et al 1998a. ); (Shi, Y.H.…”