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 0
