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 

Remora Optimization Algorithm Step-by-Step Learning with Example ~xRay Pixy

Remora Optimization Algorithm (ROA)


Remora Optimization Algorithm (ROA) is recently proposed Bionics based, Nature Inspired Metaheuristic Optimization Algorithm used to solve Global Optimization Problems. Remora Optimization Algorithm is proposed by Heming Jia, Xiaoxu Peng and Chunbo Lang in 2021. Remora Optimization Algorithm is basically inspired by the Parasitic features of remora and Random Host Replacement of remora. Remora use suction technique for their survival. They attached themselves to the host animals such as Whales, Sea Turtles, Sharks, Swordfish and other. They use their suction disk to easily attach themselves with host.

Remora clean host body from Parasites, Bacteria's, and in return they get their food for survival. They also eat the leftover food from their host. In ROA, Whale Optimization Algorithm and Swordfish Optimization Algorithm is used to update remora position in the search space. In ROA, the fusion framework is used by switching between Remora and two host (Whale, Swordfish). Remora follow 2 host Whale and Swordfish in this algorithm.
Remora Optimization Algorithm Advantages:
  • Solve Global Optimization Problems.
  • Better as compare to heuristic algorithms.
Remora Optimization Algorithm Limitations:
  • Slow Convergence Rate.
  • Poor Solution Accuracy.
  • For some engineering problems stuck in local optima.
Remora Optimization Algorithm Steps:
  • Initialize population for N remora.
  • Using fitness function evaluate performance for each remora.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position.
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.


Meta-heuristic Algorithms   CLICK HERE...

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