New Post

Life Skills for Everyday Success ~xRay Pixy

Image
Life skills are the basic abilities we need to handle daily challenges and live a healthy, balanced life. They help us think clearly, manage our feelings, make good decisions, solve problems, and build good relationships with others. The World Health Organization (WHO) highlights 10 important life skills: 1.) Thinking skills: decision-making, problem-solving, creative thinking, critical thinking 2.) Social skills: communication, empathy, interpersonal skills 3.) Emotional skills: self-awareness, coping with emotions, coping with stress Life skills are the tools that make us stronger, wiser, and calmer in real life — at home, in school, at work, and in the community :) Life Skills for Everyday Success ~xRay Pixy https://youtu.be/AMsUfKRl4kw Video Chapters: Life Skills 00:00 Introduction 01:07 Life Skills 09:42 Real Life Challenge 13:44 Task For You #LifeSkills #SuccessTools #StressFreeLiving #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swa...

Horse Herd Optimization Algorithm | Step-By-Step | ~xRay Pixy

Horse Herd Optimization Algorithm


Learn the Horse herd optimization Algorithm (HOA) Step-by-Step. - Nature Inspired Metaheuristic Optimization Algorithm - Inspired by Horse Herd Behavior. - A large number of Controlling Parameters are Used. - Used to Solve Higher Dimensional Optimization Problems in real life.

Video Chapters: Introduction: 00:00 Horse Herd Optimization Algorithm: 00:39 Horse Age Classification: 02:31 Horse Behavior: 04:28 Horse Position Update: 06:21 Horse Velocity Vectors: 08:26 Horse Grazing Vector: 09:28 Horse Hierarchy Vector: 10:38 Horse Sociability Vector: 11:45 Horse Imitation Vector: 12:30 Horse Defense Meachnism: 13:05 Horse Herd Optimization Algorithm Step: 15:06 Horse Velocity Vectors: 15:23 Horse Herd Optimization Algorithm Flowchart: 18:18 Conclusion: 19:00

A horse herd optimization algorithm is introduced in 2021. It is the nature-inspired population-based metaheuristic optimization algorithm that is basically inspired by the horse herding behavior in nature.

Disadvantage: Due to horse different behaviors, a large number of control parameters are used in this algorithm.
Advantage: We can use this algorithm to solve complex high-dimensional optimization problems.

Few Facts About Horses
  • Horse Lifespan: 15-30
  • Vision: 360 Degree
  • Brain Weight: 623 Gram
  • Male Horse Teeth: 40
  • Female Horse Teeth: 36
  • Horse Breeds: More than 300
  • Predators: Lion, Wolf, and other
Horses Behavior: Horses reveal different behavior at different ages. Different horses behavior used in this algorithm include:
  • Grazing (G)
  • Hierarchy (H)
  • Sociability (S)
  • Imitation (I)
  • Roam (R)
  • Defense Mechanism (D)
Horse Age Classification
  1. Delta Horse: Age Range Between 0-5 Years.
  2. Gama Horse: Age Range Between 5-10 Years.
  3. Beta Horse: Age Range Between 10-15 Years.
  4. Alpha Horse: Older than15 years.
Horse Herd Optimization Algorithm Pseudocode:
  1. Initialize important algorithm parameters.
  2. Initialize Population for N horses Randomly.
  3. Calculate the Fitness value for each horse in the current population.
  4. Select the best horse among all.
  5. Determine horse age: Calculate Alpha, Beta, Gama, and Delta Horse.
  6. Calculate horse velocity according to their age.
  7. Update Horse Position in the Search Space.
  8. Calculate fitness values for new solutions.
  9. Compare New Solutions with Older Solutions.
  10. Check stopping criteria.
  11. Display Best Solution Found.














Comments

Popular Post

PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

Cuckoo Search Algorithm for Optimization Problems

Particle Swarm Optimization (PSO)

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

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

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

Grey Wolf Optimization Algorithm

Grey Wolf Optimization Algorithm Numerical Example

Bat algorithm Explanation Step by Step with example

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