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
A bio-inspired algorithm which mimic the huddling behavior of Emperor Penguin.
Video Chapters:
Introduction: 00:00
What is emperor penguin optimization: 00:23
Emperor Penguin: 00:58
Emperor Penguin huddle: 01:50
Emperor Penguin Optimizer Flowchart: 03:19
Generate Emperor Penguin huddle boundary: 04:34
Calculate Temperature around huddle: 06:18
Calculate Distance between Emperor Penguins: 07:45
Relocate Effective Mover: 09:45
Emperor Penguin Optimization Steps: 10:52
Conclusion: 12:38
Emperor Penguin Optimizer is a Novel Bio-Inspired Metaheuristic Algorithm which is inspired by the huddling behavior of Emperor Penguin. Penguins are Aquatic Flightless Birds. Penguins spends their 50% life on the land and 50% life in the water. Penguin largest species is known as Emperor Penguin. Both male and female emperor penguins are similar in size. Emperor penguin is the only species which use huddle for their survival in Antarcticwinter. Huddle is used as a defense against cold to survive through though Antarctic winter. Huddling is used to conserve energy and increase temperature during winter. Without Huddle they lose energy and freeze.
Four Important phases to describehuddling behavior of Emperor penguins in Emperor Penguin Optimization Algorithm:
Step 01: Generate and determine the huddle boundary for Emperor Penguins.
Step 02: Calculate temperature around the Huddle.
Step 03: Determine the distance between the Emperor Penguins.
Step 04: Find out Effective Mover and Relocate.
Emperor Penguin Optimizer Pseudocode:Step 01: Initialize the Emperor Penguins population randomly in the search space.
Step 02: Initialize all important initial parameters such as Maximum Iterations, Temperature, A, C.
Step 03: Calculate fitness values for all search agents.
Step 04: Determine the huddle boundary for Emperor penguins using:
Step 05: Calculate temperature profile (T') around the huddle using:
Step 06: Compute distance between the emperor penguins using:
Step 07: Update the position of emperor penguins.
Step 08: If any emperor penguin goes beyond the huddle boundary improve its position.
Step 09: Calculate fitness values for each search agent and update new optimal solution position.
Step 10: If stopping criteria met Stop else Goto Step 05.
Step 11: Return Best Emperor Penguins/ Optimal Solutions.
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Particle Swarm Optimization (PSO) is a p opulation-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 upda
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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
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