Sentence examples for minimum inertia weights from inspiring English sources
In the formula, ωmaxand ωmin are the maximum and minimum inertia weights, respectively; t is the current algebra; and tmax is the maximum number of iterations.
The parameters used for PSOFCM clustering are, number of particles n = 50, maximum inertia weight w max = 0.9, minimum inertia weight w min = 0.4, c 1 = 2 and c 2 = 2 [28,29].
The weighting factor is evaluated based on the equation as follow: u = u_{hbox{max} } - frac{{u_{hbox{max} } - u_{hbox{min} } }}{{N_{iter,hbox{maxx} } }} cdot N_{iter} (22 where u max and u min are maximum and minimum value of inertia weight respectively; N iter is iteration count; N iter,max is maximum number of iterations.
The inertia weights and constriction factors in PSO were discussed in [30].
Here we used one of the best extensions of PSO that is PSO with Inertia weights (Eberhart & Shi 2000).
Reference [7] introduced novel PSO with various adaptive inertia weights during the course of the run in 2011.
To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used.
Consider (3) w L D W = w m a x − w m i n × I t e r a t i o n m a x − I t e r a t i o n i I t e r a t i o n m a x + w m i n, (4) v i d n e w = w L D W × v i d o l d + c × r 1 × p b e s t i d − x i d o l d + c × r 2 × g b e s t d − x i d o l d, (5) x i d n e w = x i d o l d + v i d n e w, where wmax is maximum value of inertia weight w and wmin is minimum value of inertia weight w.
The variation of the inertia weight in the PSO algorithm is based on the minimum and maximum expected weights as well as the current and maximum iteration step (Eq. 23).
The simulation parameter settings results of FA are: population size, 50; α (randomness), 0.25; minimum value of β, 0.20; γ (absorption), 1.0; iterations, 500; and PSO are population size of 50, the inertia weight was set to change from 0.9 ( w max ) to 0.4 (warming) over the iterations.
