The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks

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A seminar I On
Cuckoo Search Optimization
Presented By-
Miss. Anuja Joshi
Guided By-
Prof Dr. Kakandikar .G . M.
Dyanganga College Of Engineering and Research,Pune

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Types Of Algorithms

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What is Cuckoo Search?
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).

4. Cuckoo Behavior
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.
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5. Cuckoo Behavior
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
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Consequence
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.

7. Three idealized rules of
Cuckoo Search7
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 no. of available host nests is
fixed , and a host can discover an
alien egg with probability (0,1).

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Representations

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 Each egg in a nest represents a solution, and a
cuckoo egg represents a new solution.
 The aim is to use the new and potentially better
solutions (cuckoos) to replace a not-so-good
solution 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.

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Levi Flight
A Levi flight is a random walk in which the steps
are defined in terms of the step-lengths, which
have a certain probability distribution, with the
directions of the steps being isotropic and random.

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What is the algorithm?&
How does it work?
Begin
Objective function f(x), x = (x1, ...,xd)T ;
Initial a population of n host nests xi (i = 1, 2, ..., n);
while (t <Max Generation) or (stop criterion)
Get a cuckoo (say i) randomly by Lévy flights;
Evaluate its quality/fitness Fi;
Choose a nest among n (say j) randomly;
if (Fi >Fj)
Replace j by the new solution;
end
Abandon a fraction (pa) of worse nests and build new
ones at new locations via Lévy flights;
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

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Variants
Of
Cuckoo
Search
Variants Of Cuckoo Search
Improved
Cuckoo
Search

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Comparison with other
Meta Heuristic Algorithms
An important advantage of this algorithm
is its simplicity. Compared to other
metaheuristic algorithms such as particle
swarm optimization and harmony search,
there is essentially only a single parameter
in Cuckoo Search (apart from the
population size n). Therefore, it is very
easy to implement.

15. Advantages Of Cuckoo Search
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Deals with
multi-criteria
optimization
problems
Easy to
implement
Aims to
speed up
convergence
Simplicity
It can be still
hybridized
with other
swarm-based
algorithms

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Applications
spring design and welded beam design
problems
Design optimization of truss structures
Engineering optimization
Steel frames
Wind turbine blade
Reliability problems
Stability analysis
The applications of Cuckoo Search in engineering
optimization problems have shown its promising
efficiency. Some of Applications are:-

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REFERENCES
Ehsan Valian, Shahram Mohanna and Saeed Tavakoli,”
Improved Cuckoo Search Algorithm for Feedforward Neural
Network Training”, International Journal of Artificial
Intelligence & Applications (IJAIA), Vol.2, No.3, July
2011,pp.36-46
Iztok Fister Jr.a,Iztok Fistera,Xin-She Yangb,”A short
discussion about Economic optimization design of shell-
and-tube heat exchangers by a cuckoo-search-algorithm”,
International Journal of Applied Thermal Engineering 76
(2015) 535-537.
Elham Shadkam and Mehdi Bijari,” Evaluation The
Efficiency Of CuckooOptimization Algorithm”, International
Journal on Computational Sciences & Applications (IJCSA)
Vol.4, No.2, April 2014,pp.39-47.
Basu M, Chowdhury A, “Cuckoo search algorithm for
economic dispatch, Energy (2013)”, http://dx.doi.org/
10.1016/j.energy.2013.07.011,pp.1-10

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Pinar Civicioglu and Erkan Besdok,” A conceptual
comparison of the Cuckoo-search, particle swarm
optimization, differential evolution and artificial bee
colony algorithms”, © Springer Science+Business
Media B.V. 2011, 6 July 2011,pp.315-346
Manjeet Kumar, Tarun Kumar Rawat,” Optimal
design of FIR fractional order differentiator using
cuckoo search 4 algorithm”,ScienceDirect,pp.-1-17
Thang Trung Nguyen, Dieu Ngoc Vo,” Modified
cuckoo search algorithm for short-term
hydrothermal scheduling”,ScienceDirect,pp.-271-281
Janusz Piechocki , Dominik Ambroziak ,
Aleksander Palkowski , Grzegorz Redlarski,” Use of
Modified Cuckoo Search algorithm in the design
process of integrated power systems for modern and
energy self-sufficient farms”, ScienceDirect,pp.-901-
908

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