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Department Of Computer Science Engineering
Seemanta Engineering College
Jharpokharia,Mayurbhanj,757086
Cuckoo Search Algorithm
Under the supervision of Er. Abhimanyu sahu
Sambhram Basu
Roll No:2016P43
Regd No:1601201095
November 1, 2019

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presentation Outline
Introduction
Cuckoo search algorithm (History and main idea)
Behavior of Cuckoo breeding
Behavior of Cuckoo breeding (Cont.)
Characteristics of Cuckoo search
Characteristics of Cuckoo search (cont)
Levy Flights
Levy Flights (Cont.)
Cuckoo search Algorithm
Cuckoo search Algorithm (Cont)
Application of the CS Algorithm
References
Sambhram Basu | Cuckoo Search Algorithm

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Introduction
In operations research, cuckoo search is an optimization
algorithm developed by Xin-she Yang and Suash Deb in
2009.[1][2] 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). Some host birds can engage direct
conﬂict with the intruding cuckoos. For example, if a host bird
discovers the eggs are not their own, it will either throw these
alien eggs away or simply abandon its nest and build a new nest
elsewhere. Some cuckoo species such as the New World
brood-parasitic Tapera have evolved in such a way that female
parasitic cuckoos are often very specialized in the mimicry in
colors and pattern of the eggs of a few chosen host species.[3]
Cuckoo search idealized such breeding behavior, and thus can
be applied for various optimization problems.

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Cuckoo search algorithm (History and main idea)
A method of global
optimization based on the
behavior of cuckoos was
proposed by Yang Deb
(2009).
The original “cuckoo search
(CS) algorithm” is based on
the idea of the following :-
How cuckoos lay their eggs in
the host nests.
How, if not detected and
destroyed, the egg are
hatched to chicks by the
hosts.
How a search algorithm
based on such a scheme can
be used to ﬁnd the global
Figure: Yang Deb

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Behavior of Cuckoo breeding
The CS was inspired by the
obligate brood parasitism of
some cuckoo species by
laying their eggs in the nests
of host birds.
Some cuckoos have evolved
in such a way that female
parasitic cuckoos can imitate
the colors and patterns of the
eggs of a few chosen host
species.
This reduces the probability of
the eggs being abandoned
and, therefore, increases their
reproductivity .
Figure: 1

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Behavior of Cuckoo breeding (Cont.)
If host birds discover the eggs
are not their own, they will
either throw them away or
simply abandon their nests
and build new ones.
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.
Figure: 2

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Once the ﬁrst cuckoo chick is
hatched, his ﬁrst instinct
action is to evict the host eggs
by blindly propelling the eggs
out of the nest.
This action results in
increasing the cuckoo chick’s
share of food provided by its
host bird .
Moreover, studies show that a
cuckoo chick can imitate the
call of host chicks to gain
access to more feeding
opportunity. Figure: 3

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Characteristics of Cuckoo search
Each egg in a nest represents
a solution, and a cuckoo egg
represents a new solution.
The aim is to employ the new
and potentially better
solutions (cuckoos) to replace
not-so-good solutions in the
nests.
In the simplest form, each
nest has one egg.
The algorithm can be
extended to more complicated
cases in which each nest has
multiple eggs representing a
set of solutions
Figure: 4

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Characteristics of Cuckoo search (cont)
The CS is based on three
idealized rules:
Each cuckoo lays one egg at
a time, and dumps it in a
randomly chosen nest.
The best nests with high
quality of eggs (solutions) will
carry over to the next
generations.
The number of available host
nests is ﬁxed, and a host can
discover an alien egg with
probability p [0,1] .
In this case, the host bird can
either throw the egg away or
abandon the nest to build a
completely new nest in a new
Figure: 5

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Levy Flights
In nature, animals search for
food in a random or
quasi-random manner.
Generally, the foraging path of
an animal is effectively a
random walk because the
next move is based on both
the current location/state and
the transition probability to the
next location.
The chosen direction implicitly
depends on a probability,
which can be modeled
mathematically.
Figure: 6

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Levy Flights (Cont.)
A Lévy ﬂight is a random walk
in which the step-lengths are
distributed according to a
heavy-tailed probability
distribution.
After a large number of steps,
the distance from the origin of
the random walk tends to a
stable distribution.
Figure: 7

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Cuckoo search Algorithm

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Cuckoo search Algorithm (Cont)
The following steps describe the main concepts of Cuckoo
search algorithm
Step1. Generate initial population of n host nests.
Figure: 8
ai , ri : a candidate for optimal parameters

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Step2. Lay the egg ak‘, bk‘
the k nest.
K nest is randomly selected.
Cuckoo’s egg is very similar to
host egg.
where
ak‘ = ak‘+RANDOMWALK(Levyﬂight)ak
rk‘ = rk‘+RANDOMWALK(Levyﬂight)rk Figure: 9

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Step3. Compare the ﬁtness of
cuckoo’s egg with the ﬁtness
of the host egg.
Root Mean Square Error
(RMSE)
Figure: 10

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Step4. If the ﬁtness of
cuckoo’s egg is better than
host egg, replace the egg in
nest k by cuckoo’s egg.
Figure: 11

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Step5. If host bird notice it, the nest is abandoned and new one
is built. (p <0.25) (to avoid local optimization)
Figure: 12
Iterate steps 2 to 5 until termination criterion satisﬁed

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Application of the CS Algorithm
Engineering optimization problems
NP hard combinatorial optimization problems
Data fusion in wireless sensor networks
Nanoelectronic technology based operation-ampliﬁer
(OP-AMP)
Train neural network
Manufacturing scheduling
Nurse scheduling problem

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References
Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier,
(2014).
Xin-She Yang, Cuckoo Search and Fireﬂy Algorithm: Theory and
Applications, Springer, (2013).
Xin-She Yang and Suash Deb, Multiobjective cuckoo search for
design optimization, Computers Operations Research, 40(6),
1616–1624 (2013).
Some contents are taken from the original slides in
The Use of Cuckoo Search in Estimating the Parameters of
Software Reliability Growth Models 2013. 8. 7 Taehyoun Kim

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