a
) (
b
)
Figure 19.
(
a
) The optimal flight path calculated by the PSO algorithm and (
b
) the performance of the PSO algorithm under
different sizes of the swarm.
To solve the challenge, the idea of mutation from the GA was used and applied to
the TSP. Therefore, in each iteration, the EPSO algorithm generates some random new
solutions for both personal and global cases, resulting in a higher exploration rate. Figure
20a shows the optimal flight path without any intersection, and Figure 20b presents the
performance of the ESPO algorithm when the size of the swarm changes. As can be seen
from the results, regardless of the swarm size, the algorithm converges to the best mini-
mum cost function. By comparing the results of Figures 19b and 20b, EPSO can reduce the
cost function by 35% compared to PSO. In other words, the EPSO algorithm was able to
reduce the total length of the route from 79 km to 49 km.
(
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