Manta Ray Foraging Optimization (MRFO) Algorithm Example

Manta Ray Foraging Optimization (MRFO) Algorithm 

Manta Ray Foraging Optimization (MRFO) Algorithm Example

Step 01: Initialize Population Size

Suppose, Population Size = 4;

Lower Bound = -10;

Upper Bound = 10;

Maximum Iteration = 4;

Suppose Initial Population

 1.1

 2

 0.9

 3

Step 02: Compute Fitness Value for each using fitness function.

Fitness Values

1.21

4

0.81

9

Step 03: Obtain Best Solution

Best solution = Minimum Fitness Value in the current population

Best Solution = 0.81

Step 04: Check Stopping Criteria

While (Current < Maximum Iteration)

 1 < 4   ((True) move to next step ) 

If stopping criteria is then stop and return the best cost.

Step 05: Update Position for each individual.

For i = 1 to PopulationSize

For i = 1:4

If (rand < 0.5) 

THEN Cyclone Foraging

Else

Chain Foraging

End if

Step 06: Compute Fitnee Value for Each individual and Select Best Individual.

Step 07: Perform Somersault Foraging. 

Step 08: Compute Fitness Value for Each.

End For

End While

Step 09: Return Best Solution Found.