1. Scientific Research Group in Egypt (SRGE)
Flower pollination algorithm
Scientific Research Group in Egypt (SRGE)
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. Scientific Research Group in Egypt

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

4. 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.

5. 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.

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

7. 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. 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.

9. Flower pollination algorithm
Population initialization
Exploration process
Exploitation process
Solutions update

10. 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.

11. 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.

12. 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 xit , xjt are pollens (solutions) from the different lowers of the same plant species. If xit , xjt comes from the same species or selected from the same population, this become a local random walk.

13. Flower pollination algorithm (Cont.)
Step 3.4. Evaluate each solution xit+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.

14. 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

15. 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
The animated photos are taken from the following website

16. Thank you