August 2020
Intermediate to advanced
362 pages
12h 58m
English
Content preview from Swarm Intelligence Algorithms
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138 Dispersive Flies Optimisation: A Tutorial
10: end for
11: x
s
= arg min [f(x
i
)], i ∈ {0, 1, 2, . . . , N − 1} Find best fly
12: for i = 0 → N − 1 and i 6= s do Update each fly except the best
13: x
i
n
= arg min [f(x
(i−1)%N
), f(x
(i+1)%N
)] Find best neighbour
14: for d = 0 → D − 1 do Update each dimension
15: if U(0, 1) < ∆ then Restart mechanism
16: x
t+1
id
← U(x
min,d
, x
max,d
) Restart within bounds
17: else
18: u ← U(0, 1)
19: x
t+1
id
← x
t
i
n
d
+ u(x
t
sd
− x
t
id
) Update the dimension value
20: if x
t+1
id
< x
min,d
or x
t+1
id
> x
max,d
then Out of bounds
21: x
t+1
id
← U(x
min,d
, x
max,d
) Restart within bounds
22: end if
23: end if
24: end for
25: end for
26:
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.