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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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Particle Swarm Optimization (PSO)

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Particle Swarm Optimization (PSO) is a population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm. In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space.

Q. What is PSO? A. PSO is a computational method that Optimizes a problem.

Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration.

1.) The population of Candidate Solutions (i.e., Particles)

2.) The movement of Each particle is influenced by its Local Best Known Position and global best position[guided towards the best-known position in the search space].

3.) Move the swarm towards the best solution.

PSO Advantages PSO is Easy to Implement. Only a few parameters are used in PSO. Successfully applied in Function Optima, ANN training, and fuzzy control system.

PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). PSO Advantages PSO is Easy to Implement. Only a few parameters are used in PSO. Successfully applied in Function Optima, ANN training, and fuzzy control system.














#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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Comments

  1. Rajesh9 May 2021 at 22:35

    Dear Ma'am,

    I modified the cost function like

    function z = Raj(x)

    z = (x.^2+2.*x);

    end

    but coding not working

    Error in the
    BestCosts(it) = GlobalBest.Cost;

    kindly help me in this regard

    Kindly send me ur mail id so i can forward code

    ReplyDelete
    Replies
    1. Ritika Saini29 May 2021 at 09:37

      You can contact me on twitter. Change certain paraments according to your function.

      Delete

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