Sunflower optimization algorithm

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The document describes the Sunflower Optimization Algorithm (SFO), a population-based natural optimization algorithm proposed in 2019. SFO mimics the movement of sunflowers toward the sun. Each sunflower adjusts its direction and updates its position to move closer to the sun based on calculations involving distance, radiation intensity, and population size. The algorithm initializes parameters, generates an initial population randomly, evaluates individuals, selects the best as the global best, updates positions, and repeats until termination criteria are met, outputting the overall best individual.

Sunflower Optimization Algorithm
DR. AHMED FOUAD ALI
FACULTY OF COMPUTERS AND INFORMATICS
SUEZ CANAL UNIVERSITY

Outline
 Sunflower Optimization Algorithm (SFO) (History and main idea)
 Biological and natural behaviors.
 Sunflower direction adjustment.
 Sunflower position Update.
 The SFO algorithm.
 References.

Sunflower Optimization Algorithm (SFO)
(History and main idea)
 Sunflower Optimization Algorithm
(SFO) is a population based natural
inspired algorithm.
 It Proposed by G. F. Gomes, et al. in
2019.
 The SFO algorithm mimics the
movement of the sunflowers toward
the sun.

Biological and natural behaviors.
 During the movement, fertilization may be
happen between the closest neighboring
sunflowers.
 The aggregate absorbed radiation for each
sunflower is based on the distance between it
and the sun.
 Increasing the distance between the
sunflowers and the sun will reduce the
absorbed radiation for them as shown in the
following equation.
Where I is the intensity of the solar radiation, Ps is the
power of the sun and d is the distance between the sun and
each sunflower.

Sunflower direction adjustment.
 Each sunflower adjusts its direction to the
sun as shown in the following equation
Where X* is the overall best solution, Xi is the current
solution and n is the population size.
The step of individuals (sunflowers) towards the sun is
represented as follow.

Sunflower direction adjustment (Cont.)
 Each sunflower step is restricted to avoid
exceeding the following value

Sunflower position Update.
 Each sunflower (individual) i updates its
position to produce a new generation of
sunflowers based on the direction and the
step of sunflowers towards the sun as follow.
Where Xi+1 is the position of the new generated
sunflower (individual).

The SFO algorithm (Cont.)
Parameter initialization. The parameters of the
SFO algorithm are initialized such as mortality
rate m, the population size n, pollination rate P
and the maximum iteration number maxitr.
Counter initialization. The iteration counter is
initialized t := 0.
Individuals generation. The population
contains individuals which are generated
randomly Xi
(t) .
Individuals evaluation. The individuals in the
population are evaluated by calculating their
fitness value.
Assign the global best individual. The overall
best individual is assigned as the global best
solution.

The SFO algorithm (Cont.)
Individuals adjustment. Each individual adjust its
position towards the sun as show in Equation 5.
Individuals mortality. The direction vector for each
individual is calculated and the worst m%
individuals are removed.
Individuals movements. The step of each individual
towards the best individual is calculated as shown
in Equation 6 and the maximum step for each
individual is calculated as shown in Equation 7.
Individuals update. The individuals are update their
positions as show in Equation 8.

The SFO algorithm (Cont.)
Assign the new global best individual. The
individuals are evaluated and the overall best
individual is assigned.
Counter increment. The iteration number is
increased t = t + 1.
Termination criteria satisfaction. The overall
processes are repeated until termination criteria are
satisfied which are in our algorithm reaching to the
maximum number of iterations maxitr.
The global best individual production. The overall
best individual is produced.

References
G. F. Gomes, S. S da Cunha and A. C. Ancelotti. “A sunower optimization
(SFO) algorithm applied to damage identification on laminated composite
plates”. Engineering with Computers, 35(2), 619-626, 2019.

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Sunflower optimization algorithm

1. Sunflower Optimization Algorithm DR. AHMED FOUAD ALI FACULTY OF COMPUTERS AND INFORMATICS SUEZ CANAL UNIVERSITY

2. Outline  Sunflower Optimization Algorithm (SFO) (History and main idea)  Biological and natural behaviors.  Sunflower direction adjustment.  Sunflower position Update.  The SFO algorithm.  References.

3. Sunflower Optimization Algorithm (SFO) (History and main idea)  Sunflower Optimization Algorithm (SFO) is a population based natural inspired algorithm.  It Proposed by G. F. Gomes, et al. in 2019.  The SFO algorithm mimics the movement of the sunflowers toward the sun.

4. Biological and natural behaviors.  During the movement, fertilization may be happen between the closest neighboring sunflowers.  The aggregate absorbed radiation for each sunflower is based on the distance between it and the sun.  Increasing the distance between the sunflowers and the sun will reduce the absorbed radiation for them as shown in the following equation. Where I is the intensity of the solar radiation, Ps is the power of the sun and d is the distance between the sun and each sunflower.

5. Sunflower direction adjustment.  Each sunflower adjusts its direction to the sun as shown in the following equation Where X* is the overall best solution, Xi is the current solution and n is the population size. The step of individuals (sunflowers) towards the sun is represented as follow.

6. Sunflower direction adjustment (Cont.)  Each sunflower step is restricted to avoid exceeding the following value

7. Sunflower position Update.  Each sunflower (individual) i updates its position to produce a new generation of sunflowers based on the direction and the step of sunflowers towards the sun as follow. Where Xi+1 is the position of the new generated sunflower (individual).

8. The SFO algorithm.

9. The SFO algorithm (Cont.) Parameter initialization. The parameters of the SFO algorithm are initialized such as mortality rate m, the population size n, pollination rate P and the maximum iteration number maxitr. Counter initialization. The iteration counter is initialized t := 0. Individuals generation. The population contains individuals which are generated randomly Xi (t) . Individuals evaluation. The individuals in the population are evaluated by calculating their fitness value. Assign the global best individual. The overall best individual is assigned as the global best solution.

10. The SFO algorithm (Cont.) Individuals adjustment. Each individual adjust its position towards the sun as show in Equation 5. Individuals mortality. The direction vector for each individual is calculated and the worst m% individuals are removed. Individuals movements. The step of each individual towards the best individual is calculated as shown in Equation 6 and the maximum step for each individual is calculated as shown in Equation 7. Individuals update. The individuals are update their positions as show in Equation 8.

11. The SFO algorithm (Cont.) Assign the new global best individual. The individuals are evaluated and the overall best individual is assigned. Counter increment. The iteration number is increased t = t + 1. Termination criteria satisfaction. The overall processes are repeated until termination criteria are satisfied which are in our algorithm reaching to the maximum number of iterations maxitr. The global best individual production. The overall best individual is produced.

12. References G. F. Gomes, S. S da Cunha and A. C. Ancelotti. “A sunower optimization (SFO) algorithm applied to damage identification on laminated composite plates”. Engineering with Computers, 35(2), 619-626, 2019.

Sunflower optimization algorithm

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