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Python Code || Path Planning with Grey Wolf Optimization (GWO) ~xRay Pixy

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Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
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Shark Smell Optimization Algorithm Numerical Example

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SHARK SMELL OPTIMIXATION ALGORITHM [ Numerical Example ]


Shark Smell Optimization Algorithm is population based Metaheuristic optimization algorithm. Shark Smell Optimization Algorithm is inspired by the Shark food foraging behavior.  

Shark Smell Optimization Algorithm Steps:

  1. Initialize Algorithm Parameters
  2. Initialize Population for N Sharks in the search space.
  3. Evaluate Performance.
  4. While (current Iteration < Maximum Iteration)
  5. Calculate Shark Velocity
  6. Calculate Shark Position based on forward movement.
  7. Calculate Shark Position based on rotational movement.
  8. Identify Shark next position based on forward and rotational movements.
  9. Evaluate Performance.
  10. End While
  11. Display Best Solution.

STEP 01: Initial Important Parameters.

         Current_Iteration =1;

         Maximum_Iteration = 10;

         and other.

STEP 02: Initial Population Randomly.

Suppose, Population Size = 2;

Position(1) = -0.9891

Position(2) = -8.3236

STEP 03: Using Fitness Function Calculate Fitness Values.

Fitness(1) = 0.97845

Fitness(2) = 69.2819

Global Best = 0.97845

STEP 04: Calculate Shark's Velocity.

NOTE: At 1st Iteration, you can either neglect initial velocity or assign very small value.

For this Check IF (Current_Iteration ==1){ 

                Initialize Velocity.}

               Else{

                Modify Velocity.

               End

Right Now as iteration is 1.

Velocity(1) = 0;

Velocity(2) = 0;

STEP 05: Calculate Shark Movement in the search space.

Update Position for 1st Agent

          a.) Calculate Shark Forward Movement for 1st Agent.

           New Position (1) = Previous Position + Previous Velocity * Current Iteration

            New Position (1) = (-0.9891) + 0 * 1;

            New Position (1) = -0.9291

             New Fitness (1) =  0.8632

          b.) Calculate Shark Rotational Movement for 1st Agent.

          

New Position (1) = Previous Position + (Random Value * Previous Position)
New Position (1) = 0.8632 + ( 0.9189*0.8632)
New Position (1) = 1.6563
New Fitness (1) =  2.7433

Update Position for 2nd Agent

 a.) Calculate Shark Forward Movement for 2nd Agent.

           New Position (2) = Previous Position + Previous Velocity * Current Iteration

            New Position (2) = (-8.32361) + 0 * 1;

            New Position (2) = -8.3261

             New Fitness (2) =  69.2823

b.) Calculate Shark Rotational Movement for 2nd Agent.

          

New Position (2) = Previous Position + (Random Value * Previous Position)
New Position (2) = -8.3261 + ( 0.3259*(-8.3261))
New Position (2) = 5.6126
New Fitness (2) =  31.5015

STEP 06: Update Sharks Position based on Forward and Rotational Movement.

For 1st Agent:

New Position(1) = max { 0.8632,  2.7433}         //Check max value (i.e.,  0.8632) 

New Position (1) = 1.6563

New Fitness (1) =  2.7433

For 2nd Agent:

New Position(2) = max {69.2823, 31.5015}                //Check max value (i.e., 69.2823) 

 New Position (2) = -8.3261

New Fitness (2) =  69.2823

STEP 07: Increment Iteration Counter.

 Current_Iteration = Current_Iteration + 1;

 Current_Iteration = 1+1;

 Current_Iteration = 2.

NOTE: Repeat The Loop until Stopping Criteria Met.

STEP 08: Display Best Solution Found.

First Iteration, Best Solution: 69.2823 

The best position of shark is the Last Iteration that has the maximum Fitness value (for Maximization problems) is considered as the the BEST SOLUTION. 

 


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