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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Donkey and Smuggler Optimization Algorithm || STEP-BY-STEP || ~xRay Pixy
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Donkey and Smuggler Optimization Algorithm
Learn Donkey and Smuggler Optimization Algorithm Step-By-Step with Examples
Video Chapters:
Introduction: 00:00
Donkey's Behavior: 02:02
Donkey Mode: 04:20
Donkey and Smuggler Optimization Algorithm: 06:49
Smuggler Mode: 08:02
Donkey and Smuggler Optimization Algorithm STEPS: 11:54
Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39
Conclusion: 16:24
About Donkey and Smuggler Optimization Algorithm:
- Introduced in 2019 by Ahmed S Shamsaldin et. al.
- Nature Inspired Population-Based Metaheuristic Optimization Algorithm.
- Used to Solve Complex Optimization Problems.
- Implemented to solve Real Life Optimization Problems such as
Travelling Salesman Problem (TSP)
Routing Problems
Ambulance Routing
Donkey and Smuggler Optimization Algorithm MODES:
Smuggler Mode [Non-Adaptive]
Donkey Mode [Adapative]
Donkey and Smuggler Optimization Algorithm STEPS:
Initialize algorithm parameters i.e., Population Size, Dimensions, and Maximum Iterations.
Initialize the agent's position in the search space.
Calculate Fitness values for agents.
Choose the best solution among all and send the donkey.
Reevaluate the vest solution's fitness value. Check if it is a better solution or not.
If the Current Solution is NOT GOOD:
RUN: Re-evaluate the Population Fitness and update the best solution.
FACE and Suicide: Choose the second-best solution as the best solution.
FACE and Support: Use the second-best solution to support the best solution.
Video Chapters:
Introduction: 00:00Donkey's Behavior: 02:02
Donkey Mode: 04:20Donkey and Smuggler Optimization Algorithm: 06:49Smuggler Mode: 08:02Donkey and Smuggler Optimization Algorithm STEPS: 11:54Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39
Conclusion: 16:24
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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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