Avascular Necrosis (AVN) is a condition where blood flow to the bone is reduced, causing bone cells to die. This leads to pain, joint damage, and difficulty in movement, especially in the hip. Early diagnosis and proper treatment can prevent permanent bone damage and improve quality of life. Video Chapter: AVN 00:00 Introduction 00:45 What is AVN? 01:55 About Bone Tissue 02:49 AVN Causes 03:38 AVN Symptoms 04:11 AVN Diagnosis 04:56 AVN of femoral head 05:33 How AVN Develops 07:28 Conclusions #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms
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GWO In Hindi || Step-By-Step|| ~xRay Pixy
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Learn Grey Wolf Optimizer Step-by-Step using examples in Hindi.
Video Chapters: Grey Wolf Optimizer
00:00 Introduction
00:47 Topics Covered
01:28 Grey Wolf Real-life Behavior
04:35 GWO Simulation
09:36 GWO Step-By-Step
16:50 GWO Applications
17:07 GWO Advantages
17:22 GWO Disadvantages
17:29 Conclusion
Grey wolves, in the wild, have a natural ability to locate prey and encircle it during a hunt. This process is led by the alpha wolf, with occasional help from the beta and delta wolves. The remaining wolves (omegas) follow the leaders' guidance.
In optimization problems, however, the location of the optimal solution (the "prey") is unknown. To mimic this behavior in the Grey Wolf Optimizer (GWO), we make some assumptions:
Alpha, beta, and delta are considered the top three best solutions found so far.
These three "leader wolves" guide the movement of all other solutions (search agents or omegas).
Grey Wolf Optimizer (GWO) is directly inspired by the social hierarchy and hunting strategy of grey wolves, as described in the story. This fascinating metaheuristic algorithm mimics the way grey wolves organize themselves and work together during hunting, making it a powerful tool for solving optimization problems.
Why is GWO effective?
Simplicity: The algorithm is easy to understand and implement.
Balance: GWO balances exploration (searching the solution space) and exploitation (refining solutions).
Adaptability: Inspired by nature, it can adapt to diverse optimization problems.
Applications of Grey Wolf Optimizer (GWO):
Engineering Optimization:
Structural design, power systems optimization, and robotics path planning.
Machine Learning:
Feature selection, neural network training, and clustering.
Networking:
Routing optimization, load balancing, and resource allocation.
Healthcare:
Medical image segmentation and treatment planning.
Finance:
Portfolio optimization and risk management.
Advantages of GWO:
Simplicity:
Easy to implement with fewer parameters compared to other algorithms.
Exploration-Exploitation Balance:
Dynamically transitions from exploration to exploitation.
Global Search Ability:
Reduces the risk of getting stuck in local optima.
Flexible for Complex Problems:
Effective in continuous and discrete optimization problems.
Few Parameters:
Reduces the burden of tuning hyperparameters.
Disadvantages of GWO:
Slow Convergence:
May take more iterations to reach the optimal solution.
Dependence on Parameters:
Performance can be sensitive to initialization and random parameters.
Lack of Diversity in Later Stages:
May lead to premature convergence if wolves cluster too closely.
Scalability Issues:
Performance may degrade with high-dimensional problems.
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Whale Optimization Algorithm Code Implementation Whale Optimization Algorithm Code Files function obj_fun(test_fun) switch test_fun case 'F1' x = -100:2:100; y=x; case 'F2' x = -10:2:10; y=x; end end function [LB,UB,D,FitFun]=test_fun_info(C) switch C case 'F1' FitFun = @F1; LB = -100; UB = 100; D = 30; case 'F2' FitFun = @F2; LB = -10; UB = 10; D = 30; end % F1 Test Function function r = F1(x) r = sum(x.^2); end % F2 Test Function function r = F2(x) r = sum(abs(x))+prod(abs(x)); end end function Position = initialize(Pop_Size,D,UB,LB) SS_Bo...
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 Optimiza...
Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 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 the 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 Grey Wolf Optimization Algorithm Numerical Example STEP 1. Initialize the Grey wolf Population [Initial Position for each Search Agent] 𝒙_(𝒊 ) (i = 1,2,3,…n) n = 6 // Number of Search Agents [ -100, 100] // Range Initial Wolf Position 3.2228 4.1553 -3.8197 4.2330 ...
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