Analysis Of Figure 3.3 Arrangment Of Birds In Swarm
Every particle in the algorithm acts as a point in the N-dimensional space. Each particle keeps the information in the solution space for each iteration and the best solution is calculated, that has obtained by that particle is called personal best (pbest). This solution is obtained according to the personal experiences of each particle vector. Another best value that is tracked by the PSO is in the neighborhood of that particle and this value is called gbest among all pbests. Figure 3.3 Arrangment of Birds in Swarm
Throughout the process, each particle i monitors three values: its current position (x_i^k); the best position it reached in previous cycles (pbest_i); its flying velocity (w^(k+1)). These three values are represented as follows …show more content…
It is employed to control the impact of the previous history of velocities on the current velocity. It directs the trade-off between global and local exploration abilities of the flying points. A larger inertia weight wmax facilitates global exploration while a smaller inertia weight wmin tends to facilitate local exploration to fine-tune the current search area [2]. w^(k+1)=w_max-(w_max-w_min )* ((k+1))/k_max (3.8) Where kmax= Maximum number of the iteration cycles.
3.4.4. Optimization Parameters in the Particle swarm Optimization
Like all heuristics, parameter selection is critical to PSO performance. PSO has five parameters [8]: Population Size (nPop) Maximum Change in Particle Velocity …show more content…
The value of velocity is clamped to the range [−vmax, vmax] to reduce the likelihood that the particle might leave the search space. The problem, according to Eberhart and Shi, is that the particles stray too far from the desired region of search space. To mitigate this effect they decided to apply clamping to the constriction factor implementation as well, setting the vmax parameter equal to xmax, the size of the search space. This led to improved performance for almost all the functions they used during testing — both in terms of the rate of convergence and the ability of the algorithm to reach the error
Throughout the process, each particle i monitors three values: its current position (x_i^k); the best position it reached in previous cycles (pbest_i); its flying velocity (w^(k+1)). These three values are represented as follows …show more content…
It is employed to control the impact of the previous history of velocities on the current velocity. It directs the trade-off between global and local exploration abilities of the flying points. A larger inertia weight wmax facilitates global exploration while a smaller inertia weight wmin tends to facilitate local exploration to fine-tune the current search area [2]. w^(k+1)=w_max-(w_max-w_min )* ((k+1))/k_max (3.8) Where kmax= Maximum number of the iteration cycles.
3.4.4. Optimization Parameters in the Particle swarm Optimization
Like all heuristics, parameter selection is critical to PSO performance. PSO has five parameters [8]: Population Size (nPop) Maximum Change in Particle Velocity …show more content…
The value of velocity is clamped to the range [−vmax, vmax] to reduce the likelihood that the particle might leave the search space. The problem, according to Eberhart and Shi, is that the particles stray too far from the desired region of search space. To mitigate this effect they decided to apply clamping to the constriction factor implementation as well, setting the vmax parameter equal to xmax, the size of the search space. This led to improved performance for almost all the functions they used during testing — both in terms of the rate of convergence and the ability of the algorithm to reach the error