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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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Grey Wolf Optimization Algorithm

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 Grey Wolf Optimization Algorithm  (GWO) Grey Wolf Optimization

Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves.

What is Metaheuristic?

Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization.

Who are the Grey Wolves?

Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild).

Scientific Name: Canis Lupus.

Family: Canidae (Biological family of dog-like carnivorans).

Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12.  4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf.

How Grey Wolf Optimization Algorithm Works?

Grey wolf Optimization algorithm mimics the Leadership and Hunting Mechanism of grey wolves. Main Steps of Grey Wolf Hunting are:

1.) Searching for the Prey. 

2.) Tracking, Chasing & Approaching the Prey. 

3.) Pursuing, Encircling, and Harassing the Prey until it stops moving. 

4.) Attacking the Prey.

Large animals like moose may stand their ground and fight. Wolf may choose to try other prey rather than risk attack on large animals willing to fight. The hunting process is guided by Alpha. It is assumed that α, β, δ have better knowledge about the location of prey (i.e., the optimal solution). Other wolves will update their positions according to the position of α, β, δ. 

Grey Wolf Optimization Algorithm and its Flowchart.

1.) Initialize Grey Wolf Population.

2.) Initialize a, A, and C.

3.) Calculate the fitness of each search agent.

4.) 𝑿_𝜶 = best search agent

5.) 𝑿_𝜷 = second-best search agent

6.) 𝑿_𝜹 = third best search agent.

7.) while (t<Max number of iteration)

 8.) For each search agent 

     update the position of the current search agent by above equations

end for

9.) update a, A, and C

10.) Calculate the fitness of all search agents.

11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹

12.) t = t+1

end while

13.) return 𝑿_𝜶

GWO Flow chart


Software Testing using Metaheuristic Optimization

Test Suite Prioritization Problem Solved using Grey Wolf Optimization Algorithm.


Topics Covered in this Video:

INTRODUCTION TO SOFTWARE ENGINEERING
SOFTWARE DEVELOPMENT LIFE CYCLE
SOFTWARE TESTING
SOFTWARE TESTING OBJECTIVE
SOFTWARE TESTING LEVELS
SOFTWARE TESTING TOOLS
SOFTWARE TESTING USING METAHEURISTIC OPTIMIZATION ALGORITHMS
TEST SUITE PRIORITIZATION PROBLEM
TEST SUITE PRIORITIZATION USING OPTIMIZATION ALGORITHMS
SOFTWARE TESTING CHALLANGES
SOFTWARE TESTING DESIGN STTATIES
SEARCH BASED SOFTWARE TESTING
WHITE BOX TESTING
BLACK BOX TESTING
TEST SUITE DESIGN EXAMPLE
TEST SUITE
TEST SUITE PRIORITIZATION

Applications of Grey Wolf Optimization Algorithm. 

Grey wolf optimization algorithm is used to solve different real-world optimization problems.

Gray wolf Optimization Algorithm (GWO) |for FITNESS VALUE, POPULATION|MATLAB|(Part - 2)~xRay Pixy

https://youtu.be/-ZLeQ4KcBTY


Gray wolf Optimization Algorithm (GWO) Step-By-Step Explanation with Example (PART 1) ~xRay Pixy

https://youtu.be/mmSat8WEgCA


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