New Post

Avascular Necrosis (AVN) || Early Detection, Better Outcomes || ~xRay Pixy

Image
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 

Multiverse Optimization Algorithm Example Step-by-Step Explanation

Multiverse Optimization Algorithm

 

Q. What is Multiverse Theory?
A. Multiverse means the group of multiple universes. According to Multiverse theory, there is more than one Big Bang, and each Big Bang causes the birth of a universe. Multiple universes collide and interact with each other. 

Q. What is Big Bang Theory?
A. Our universe comes into existence (13 Billion years Ago) from a single, hot and dense point. According to the Big Bang theory, our universe starts with a massive explosion. Before this explosion, nothing exists in this world. The universe contains time, energy, planets, stars, galaxies, and matter.

Q. What is Multiverse Optimization Algorithm?
A. Multiverse Optimization Algorithm (MVO) is inspired by multiverse theory. Multiverse optimization algorithm is basically inspired by three main concepts of multiverse theory: White Hole, Black Hole, and Worm Hole. According to multi-verse theory, white holes / Big Bangs are created where collisions between parallel universes occurred. A black Hole can attract everything (including light) with its gravitational force.  Wormhole act as a travel tunnel where objects can travel from one universe to another.

Q. State certain rules that are applied to the universe of MVO.
A.  Certain rules are followed in the multiverse optimization algorithm during optimization process:
  1. The Higher the inflation rate, the higher the probability of having a white hole.
  2. The Higher the inflation rate, the lower the probability of having a black hole.  
  3. Universes with a higher inflation rate tend to send objects through white holes. 
  4. Universes with a lower inflation rate tend to receive more objects through black holes. 
  5. The objects in all universes may face random movement towards the best universe via wormholes regardless of the inflation rate. 
Q. How universes exchange objects in MVO?
A. Wormholes act as a tunnel in between the white holes and black holes. Assume that wormhole tunnels are always established between a universe and the best universe. (to provide local changes to each universe). The mathematical model is:

Q. Multiverse Optimization Algorithm Step-by-Step
A. Input: Population Size and Maximum Number of iterations.
Output: Display Best Universe and its Fitness Value / Inflation rate.
Step 01: Parameter Initialization. 
Step 02: Compute Fitness Value for Each Universe and Select Best Universe. 
Step 03: For each universe update WEP and TDR. 
Step 04: Select one universe among N by roulette wheel selection mechanisms as a white hole.
Step 05: Use a wormhole as a tunnel for object exchange between different universes. 
Step 06: Repeat until stopping criteria matched.
Step 07: Display the best universe. 

Q. Multiverse Optimization Algorithm Applications.
A. Multiverse optimization algorithms can be applied to solve complex problems in different fields.
  1. Network applications.
  2. Engineering problems
  3. Machine learning problems  

Comments

Popular Post

PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

Cuckoo Search Algorithm for Optimization Problems

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

Particle Swarm Optimization (PSO)

PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

Grey Wolf Optimization Algorithm

Grey Wolf Optimization Algorithm Numerical Example

GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy