Cuckoo Search By Biswajit Panday

1. CUCKOO SEARCH
By : Biswajit Panday
1
Ahsanullah University of Science & Technology
Email : biswajitmailid@gmail.com

2. 2
Cuckoo search (CS) is an optimization
algorithm developed by Xin-she Yang
and Suash Deb in 2009.
It was inspired by the obligate brood parasitism of some
cuckoo species by laying their eggs in the nests of other
host birds (of other species).

3. 3
 Cuckoos have an aggressive reproduction strategy that involves the
female laying her fertilized eggs in the nest of another species so that
the surrogate parents unwittingly raise her brood. Sometimes the
cuckoo's egg in the host nest is discovered (eggs are not its owns), the
surrogate parents either throw it out or abandon the nest and builds
their own brood elsewhere.
 Some cuckoo species have evolved in such a way that female parasitic
cuckoos are often very specialized in the mimicry in color and pattern
of the eggs of a few chosen host species. This reduces the probability
of eggs being abandoned and increases their reproductively.

4. 4
 Parasitic cuckoos often choose a nest where the host bird just
laid its own eggs. In general, the cuckoo eggs hatch slightly
earlier than their host eggs.
Recently laid
egg by other
species.

5. 5
 Once the first cuckoo chick is hatched, the first instinct action
it will take is to evict the host eggs by blindly propelling the
eggs out of the nest, which increases the cuckoo chick’s share
of food provided by its host bird.

6. 6
1) Each cuckoo lays one egg at a time, and dumps it in a randomly
chosen nest.
2) The best nests with high quality of eggs (solutions) will carry
over to the next generations.
3) The number of available host nests is fixed, and a host can
discover an alien egg with a probability pa ∈ [0, 1]. In this case,
the host bird can either throw the egg away or abandon the nest
so as to build a completely new nest in a new location.

7. 7
Begin
Objective function f(x), x = (x1, ..., xd)T ;
Initial a population of n host nests xi (i = 1, 2, ..., n);
while (t <MaxGeneration) or (stop criterion)
Get a cuckoo (say i) randomly by Lévyflights;
Evaluate its quality/fitness Fi;
Choose a nest among n (say j) randomly;
if (Fi > Fj)
Replace j by the new solution;
end
A fraction (pa) of worse nests are abandon and
new once are built.
Keep the best solutions (or nests with quality solutions);
Rank the solutions and find the current best;
end while
Post-process results and visualization;
End

8. 8
Start
End
Initialize population size
Initialize the initial fitness
Iteration<max &max
fitness>accuracy
Calculate center of chromosome
Calculate number of eggs
Calculate radius
Calculate radiuses
Determine position of best chromosome to be copied to next generation
Apply crossover
Make clusters and determine the best clusters
Update the chromosome and copy the best one to the new generation

9. 9
a) Spring design and Welded beam design problems.
b) Solve nurse scheduling problem.
c) An efficient computation for data fusion in wireless sensor
networks.
d) A new quantum-inspired cuckoo search was developed to
solve Knapsack problems.
e) Efficiently generate independent test paths for structural
software testing and test data generation.
f) Applied to train neural networks with improved
performance.

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