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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 herdin

Video Chapters: Krill Herd Optimization Algorithm Introduction: 00:00 KHO Parameters: 00:51 Krill's Position Initialization: 01:51 Objective Function Calculation: 03:52 Conclusion: 05:22 Learn How to Calculate Objective Function values for Metaheuristic Optimization Algorithm. Objective Function is also known as Cost Function, Fitness function, or Evaluation Function. Krill herd Optimization Algorithm Introduction, Numerical Examples: https://www.youtube.com/playlist?list=PLVLAu9B7VtkYR8GkHtTHV83AlR0WjGCfi Initialize the position for search agents randomly in the search space using this equation: Agent's Position in the Search Space : Using any Objective Function to calculate fitness values for each agent: Sphere Function is used here Fitness Values for each agent: Fitness(1) = 4.11424

Metaheuristics Search Space Design and Standard Testing Functions Video Chapters: Introduction: 00:00 Metaheuristics Testing Functions: 01:05 Metaheuristics Search Space: 05:40 Conclusion: 10:00 Metaheuristics Standard Benchmark Functions for Testing - Unimodel Test Functions - Multimodel Test Functions - Fixed Dimensions Test Functions - Hybrid Test Functions - Composition Test Functions Metaheuristics Search Space Design - Search Space - Search Space Bounds Metaheuristic Search Space Design and Meta-heuristic testing functions: Different testing functions that we can use for the comparison between different metaheuristic algorithms and we can analyze algorithm Performance, Stability, Convergence Speed, Accuracy, Efficiency and other. Unimodal test functions are used to check algorithm convergence property and Exploitation capability and the fitness curve in the unimodal test function is used to check the algorithm convergence speed. Multimodal test functions are used to chec

Metaheuristics Performance Comparison Comparison of the Metaheuristic Algorithms Performances Video Chapters: Introduction: 00:00 Metaheuristic Algorithms Applications: 01:41 Metaheuristics Comparision: 02:43 Testing Parameters: 06:16 Testing Output: 07:24 Optimal Values: 09:00 Computation Time: 12:00 Conclusion: 13:20 Questions Answered in this Video: Which is the best metaheuristic algorithm? Learn how to Compare Metaheuristic Optimization Algorithms Performance using the Standard Test Functions. Standard Test Functions used for Testing: - Unimodel Test Functions - Multi-Model Test Functions Metaheuristics Algorithms used for Comparision: - Particle Swarm Optimization (PSO) Algorithm - Jaya Optimization Algorithm (JOA) Testing Output is checked based on: - Optimal Value - Computation Time Using these test functions we will analyze the algorithm's Stability, Convergence Speed, and Accuracy. The comparison between the two metaheuristics is done using the standard test functi

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Hybrid Teaching Learning Based Optimization and Harmony Search Hybrid TLBO-HS Video Chapters: Introduction: 00:00 Metaheuristics Hybridization: 00:40 Hybrid TLBO-HS: 02:43 Hybrid TLBO-HS Steps: 05:17 Conclusion: 11:30

Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm GOA-GA Hybrid Metaheuristics Video Chapters: Introduction: 00:00 Hybrid Metaheuristics: 01:11 Metaheuristic Hybridization Types: 02:47 Hybrid Grasshopper Optimization Algorithm - Genetic Algorithm: 04:06 Hybrid Grasshopper and Genetic Algorithm Steps: 08:54 Conclusion: 13:00 Hybrid methods are powerful as compared to others. Suppose, we have 2 Algorithms: Algorithm A and Algorithm B . Now suppose we merge the merits of both algorithms and formed Hybrid A-B Algorithm. New Algorithm i.e., Hybrid A-B Algorithm is better as compared to Algorithm A or Algorithm B. Metaheuristics Hybridization Types: Metaheuristic with Metaheuristic. Metaheuristic with Exact Methods. Metaheuristic with Constraint Programming, Artificial Intelligence. Metaheuristic with Data Mining and Machine Learning Techniques. Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm |Hybrid G.O.A - G.A.| Hybrid GOA-GA is a combination of 2 Meta-h

Jellyfish Search Optimizer (2020) Video Chapters: Introduction: 00:00 Jellyfish Search Optimizer: 00:26 About Jellyfish: 01:11 Jellyfish Search Optimize Steps: 06:37 Time Control Calculation: 10:50 Passive Motion: 11:59 Action Motion: 12:52 Ocean Current: 15:27 Conclusion: 16:31 Jellyfish: Sea Animals Without Backbone. Jellyfish Size: 1-16 inch. Jellyfish Lifespan : 1 Month / 1 Year depend on species. Jellyfish Diet : Nutrients Plants, Planktons, Small Fishes, Fish eggs. Jellyfish Search Optimizer is also known as artificial Jellyfish Search Optimizer. Jellyfish Search Optimizer is inspired by jellyfish food searching behavior in the ocean. We can use Jellyfish Search Optimizer to solve Global Optimization problems, Complex real world optimization problems and other. Jellyfish movements are result due to: Ocean Current ( Horizontal Movement and Vertical Movemen t). Jellyfish Motion inside Swarm ( Passive Motion and Active Motion ). Time Control mechanism is used to switch jellyfish

  Video Chapters: Introduction: 00:00 Aquila Optimizer: 00:31 Aquila Hunting Methods: 02:09 Aquila Optimizer Steps: 03:33 Aquila Optimizer Mathematical Models: 06:07 Conclusion: 13:00 Aquila Optimization Algorithm is inspired by the Aquila Behavior in the nature. This algorithm is basically inspired by the aquilas hunting methods. How they catch their prey in the real life? Aquila Hunting Methods: Method 01: High Soar with Vertical Stoop. [i.e., Expanded Exploration] Method 02: Contour Flight with Short Glide Attack. [i.e., Narrowed Exploration] Method 03: Low Flight with Slow Decent Attack. [i.e., Expanded Exploitation] Method 04: Walking and Grab the Prey. [i.e., Narrowed Exploitation] Aquila Optimization Algorithm Steps: Step 01: Initialize Algorithm Parameters and Population Randomly. Step 02: Check While (Current Iteration <= Maximum Iteration) Step 03: Evaluate Agents Performance using Fitness Function. Step 04: For all agents update Location mean value. Step 05: Update Levy&#