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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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PSO (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO)

What is meant by PSO?

PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n 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. 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.

How PSO will optimize?

By Improving a Candidate Solution.

How PSO Solve Problems?
PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles).

What is Search Space in PSO?

It is the range in which the algorithm computes the optimal control variable. When any optimal control value of any particle exceeds the searching space, the value will be reinitialized. 

PSO Disadvantage: PSO algorithm do not guarantee an optimal solution is ever found

What is the PSO fitness value?
Fitness Function is used in Metaheuristic Algorithms for OPTIMIZATION.

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How to Evaluate Fitness Values for each Particle?
By Fitness Function. 
What is PSO used for?
To solve Optimization problems.
What is the global best in PSO?
First Best One is the Best Solution.

How does swarm intelligence work?
Follow the Bird Which is Nearest to the Food.

PSO Search Strategy: Follow the Bird Which is Nearest to the Food.

Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2]

Comments

  1. Mary Brown13 September 2021 at 00:57

    great

    ReplyDelete
  2. Lalit mohan dubey10 October 2023 at 04:44

    can you provide the code for finding the life time of each sensor node by using PSO and Grass hoper lgorithm

    ReplyDelete

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