Research Article
Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil
| FOR each particle i in swarm | | Initialize parameters: , _damp, c1, c2. | | FOR each dimension j | | Initialize position xij randomly within permissible range | | Initialize velocity randomly within permissible range | | End FOR | | End FOR | | Iteration k = 1 | | WHILE k < maximum_Iteration | | FOR each particle i in swarm | | Calculate fitness value | | IF fitness value > P_best[i] THEN | | P_best[i] = fitness value | | END IF | | IF fitness value > G_best THEN | | G_best = fitness value | | END IF | | END FOR | | FOR each particle i in swarm | | FOR each dimension j | | Calculate new velocity: | | (k + 1) = wvij(k) + c1random (0,1) (P_best[i] − xij[i]) + c2random (0,1) (G_best − xij[i]) | | Update particle positon: | | xij(k + 1) = xij(k) + (k + 1) | | END FOR | | END FOR | | = ._damp | | k = k + 1 | | END WHILE |
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