Flowerpollination 141114212025-conversion-gate02

1. Company
LOGO
Scientific Research Group in Egypt (SRGE)
Flower pollination algorithm
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt .

2. Company
LOGO Scientific Research Group in Egypt
www.egyptscience.net

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LOGO Outline
1. Flower pollination algorithm (History and main idea)
3. Flower pollination algorithm behavior
2. Characteristics of flower pollination
6. References
4. Flower pollination algorithm
5. Application of the flower pollination algorithm

4. Company
LOGO Flower pollination algorithm (History and main idea)
•Flower pollination algorithm (FPA) is a nature-
inspired population based algorithm proposed by
Xin-She Yang (2012).
•The main objective of the flower pollination is to
produce the optimal reproduction of plants by
surviving the most fittest flowers in the flowering
plants.
•In fact this is an optimization process of plants in
species.

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LOGO Characteristics of flower pollination
•There are over a quarter of a million types of
flowering plants in Nature, 80% of them are
flowering species.
•The main purpose of a flower is ultimately
reproduction via pollination.
•Flower pollination process is associated with
the transfer of pollen by using pollinators such
as insects, birds, bats,...etc.

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LOGO Characteristics of flower pollination (Cont.)
•There are two major process for transferring the pollen
Biotic and cross pollination process.
Abiotic and self pollination Process
Cross pollination process
Self pollination Process

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LOGO Characteristics of flower pollination (Cont.)
Biotic and cross pollination process.
•Biotic pollination represents 90% of flowering
plants, while 10% of pollination takes from
abiotic process.
•In the biotic pollination, pollen is transferred
from one flower to other flower in different plant
by a pollinator such as insects, birds, bats,…etc.
•Biotic, cross-pollination may occur at long
distance and they can considered as a global
pollination process with pollinators performing
Le'vy flights.

8. Company
LOGO Characteristics of flower pollination (Cont.)
Abiotic and self pollination Process
•On the other hand, abiotic or self pollination
process is a fertilization of one flower from
pollen of the same flower of different flower of
the same plant.
• In this type of pollination, wind and diffusion
in water help pollination of such flowering
plants.
•Abiotic and self pollination process are
considered as local pollination.

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LOGO Flower pollination algorithm Population
initialization
Exploration
process
Exploitation
process
Solutions update

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LOGO Flower pollination algorithm (Cont.)
Step 1. The algorithm starts by setting the initial values of the most
important parameters such as the population size n, switch
probability p and the maximum number of generations MGN.
Step 2. The initial population xi, i = 1,…,n is generated randomly
and the fitness function of each solution f(xi) in the population is
evaluated by calculating its corresponding objective function.
Step 3. The following steps are repeated until the termination
criterion satisfied, which is to reach the desired number of
generations MGN.

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LOGO Flower pollination algorithm (Cont.)
Step 3.1. The global pollination process is started by generating a
random number r, where rϵ[0,1], for each solution xi.
Step 3.2. If r < p, where p is a switch probability, the new solution is
generated by a Le'vy distribution as follow.
Where L is a Le'vy flight, L > 0 and calculated as follow.

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LOGO Flower pollination algorithm (Cont.)
• Γ(λ) is the standard gamma function and this distribution is valid
for large steps s > 0.
Step 3.3. Otherwise, the local pollination process is started by
generating a random number ϵ, ϵ in [0,1] as follow
Where xi
t , xj
t are pollens (solutions) from the different lowers of
the same plant species. If xi
t , xj
t comes from the same species or
selected from the same population, this become a local random
walk.

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LOGO Flower pollination algorithm (Cont.)
Step 3.4. Evaluate each solution xi
t+1 in the population and update
the solutions in the population according to their objective values.
Step 3.4. Rank the solutions and find the current best solution g*.
Step 4. Produce the best found solution so far.

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LOGO Application of the FP 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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LOGO References
Yang, X. S. (2012), Flower pollination algorithm for global
optimization, in: Unconventional Computation and Natural
Computation, Lecture Notes in Computer Science, Vol. 7445, pp.
240-249.
The animated photos are taken from the following website
http://www.fs.fed.us/wildflowers/pollinators/index.shtml

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LOGO
Thank you
http://www.egyptscience.net
Ahmed_fouad@ci.suez.edu.eg