

![0_image_0.png](0_image_0.png)

The objective function (in Figure 4) did not improve much after 100 iterations even thou the global model changed. A relatively small improvement occurred (not shown) due to model m6 becoming the global best model after 300 iterations. It is clear from figure 5 that the objective function had a significant role in the updating of the model parameters. Initially model m3 was the pg then the global best changed to being m6, m2, m4, m7, m2 then finally model m6 was again the pg. The final objective function based model order in this simulation was m6, m1, m2, m5, m4, m8, m3 and then m7. This is different to the results in simulation 1 where the less complex models attained lowest errors. 5.1.3 Simulation number 3 In simulation number 3 the PSO algorithm has been changed by introducing the adaptive inertia weight. As mentioned in section 2.1 the inertia parameter allows the algorithm to initially explore a wider search area and near the end to exploit the local search space. 

![0_image_1.png](0_image_1.png) 

![0_image_2.png](0_image_2.png) 
