Manta Ray Optimization.pptx

1. MANTA RAY FORAGING OPTIMIZATION
(MRFO) ALGORITHM
DR. AHMED FOUAD ALI
FACULTY OF COMPUTERS AND INFORMATICS
SUEZ CANAL UNIVERSITY

2. Outline
 Manta ray foraging optimization (MRFO) algorithm
(History and main idea)
 Inspiration and the foraging behavior of the manta rays
 The mathematical model of MRFO algorithm
Chain foraging strategy
Cyclone foraging strategy
Somersault foraging strategy
 Pseudo-code of the MRFO algorithm
 References.

3. Manta ray foraging (MRFO) algorithm (History and main idea)
 Manta ray foraging optimization (MRFO) is a
recent natural-inspired algorithm that emulates the
foraging action of manta ray creatures.
 It was proposed by Weiguo Zhao et al. in 2020

4. Inspiration and the foraging behavior of the manta rays
 Manta rays (MRs) live in oceans, and they travel alone
or in a group searching for food.
 Every day they feed on a huge amount of plankton
which is found in abundance in the oceans.
 MRs can be found in different sizes from 5.5 to 7 meters
in width and their age can span till 20 years.
 MRs have not sharp teeth, but they use horn-shaped
cephalic lobes to duct water and plankton into their
mouth after that the food is refined from the water by
modulated gill rakers.
 MRs use three smart foraging strategies (tactics) which
are chain, cyclone, and somersault strategies to catch
their prey.

5. Inspiration and the foraging behavior of the manta rays (Cont.)
 In the chain foraging, MRs line up to form a regulated line one
behind another. This strategy can help the MRs on behind to
scoop up the missed plankton from the previous ones.
 Cyclone foraging is the second foraging strategy of MRs.
 If the amount of plankton is huge, they group and link up their
tail end with heads to form a spiral-like eye of the cyclone.
 This strategy can help the manta rays to extract the plankton
from the filtered water and pull it into their mouth.
 The last foraging strategy is somersault foraging.
 MRs use this strategy when they found the source of food.
 They start a series of random cyclical movements around the
food (plankton) to pull it to their mouth.

6. The mathematical model of MRFO algorithm
Chain foraging strategy
 MRs are searching for the highest density of plankton
(food), which represents the best position in the search.
 In MRFO, the position with the highest density of
plankton is the position of the best solution.
 MRs form a foraging chain from head to tail.
 The first individual in the chain changes its location
based on the location of the superior solution as shown
in the following equation

7. The mathematical model of MRFO algorithm
Chain foraging strategy (Cont.)

8. The mathematical model of MRFO algorithm
Chain foraging strategy (Cont.)
 The individuals from the second till the last in the chain
update their position based on the superior position
(food) and the individual in front of each one as shown
in the following equation.

9. The mathematical model of MRFO algorithm
Cyclone foraging strategy
 MRs move in a spiral way to the best position (food)
if it is found in the deep water.
 Each individual in the group (school) pursues the
individual in front of it and the position of the food.
 The helix movement of MRs in the 2-D search space
can be represented as shown in the following
equations

10. The mathematical model of MRFO algorithm
Cyclone foraging strategy (Cont.)
 The general form of Equations 5, 6 in n-D search space
can be represented as follow.
 The first individual in the chain changes its location
according to the location of the superior individual as
shown in Equation 7.

11. The mathematical model of MRFO algorithm
Cyclone foraging strategy (Cont.)
 The individuals from the second till the last in the chain change their location
based on the location of food and the individual in front of each one as shown in
Equation 8.

12. The mathematical model of MRFO algorithm
Cyclone foraging strategy (Cont.)

13. The mathematical model of MRFO algorithm
Cyclone foraging strategy (Cont.)
 The first individual in the chain changes its location according to the
location of the superior individual as shown in Equation 10.
 The individuals from the second till the last in the chain change their
location based on the best position (food) and the individual in front of
each one as shown in Equation 11.

14. The mathematical model of MRFO algorithm
Somersault foraging strategy
 In this strategy, the best individual (food) is
considered as a pivot.
 All individuals somersault around the pivot move to
a new location.
 The mathematical model of the somersault foraging
strategy can be defined as follow.

15. The mathematical model of MRFO algorithm
Somersault foraging strategy (Cont.)

16. Pseudo-code of the MRFO algorithm

17. Pseudo-code of the MRFO algorithm (Cont.)

18. Pseudo-code of the MRFO algorithm (Cont.)

19. References
Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging
optimization: An effective bio-inspired optimizer for engineering
applications. Engineering Applications of Artificial Intelligence, 87,
103300