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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 Le...

 Cat and Mouse Optimization Algorithm (CMOA) Cat and Mouse Optimization Algorithm is a population based metaheuristic optimization algorithm. Cat and Mouse Optimization Algorithm mimic the natural behavior of Cat attack on the mouse and Mouse escape from the Cat. In this algorithm population is divided into 2 groups: Group of Cats and Group of Mice . Cat and Mice scan the whole search space in this algorithm with their random movements. Each member in the population is a solution to the given problem. Initial population is evaluated using objective function and based on their fitness values population is sorted. Best values in the population as calculated using objective function are considered as Population for Mice and worst values in the population are considered as Population for Cats . Position Update Procedure in Cat and Mouse Optimization Algorithm (CMOA): Position Update in CMOA is divided into 2 phases as given below: First, Move Cats Towards Mice. Second, Move Mic...

Harmony Search Algorithm Video Chapters: Introduction: 00:00 Harmony Search Algorithm: 01:00 Harmony Search Algorithm Example 1: 04:00 Harmony Search Algorithm Numerical Example 2: 07:26 Harmony Search Algorithm Numerical Example 3: 11:36 Conclusion: 15:00 How does harmony search algorithm work? Harmony Search Algorithm (HSA) is introduced by Zong Woo Geem and Joong Hoon Kim in 2001. Harmony Search is a music inspired optimization algorithm. Harmony Search Algorithm is basically inspired by the Music Harmony. Music Harmony refers to the relationship between sound waves coming either from musical instruments or human voices. It is the process by which individual sounds are joined together simultaneously. It is the combination of sound pitches in the music. Pitch is an aspect of sound that we can hear. Through pitch we can check weather sound is High or Low than other musical sound. Harmony Search Algorithm Main Rules: Musician has 3 choices Play Famous piece of Music (i.e., kn...

 Krill Herd Optimization Algorithm Numerical Example KRILL HERD OPTIMIZATION ALGORITHM STEPS Initialize Algorithm Parameters. Initialize Population for Krill's. Evaluate Krill's Performance. Selection Best Krill Among all. Check While (Current Iteration <= Maximum Iteration) Calculate Neighbors Krill Effect. Movement Induced. Calculate Food Attraction. Calculate Best Position Attraction. Foraging Motion. Physical Diffusion. Crossover and Mutation Update Krill Position. End While Display Best Solution Movement Induced Calculation Foraging Motion Calculation Physical Diffusion Calculation What is Krill Herd Optimization Algorithm?  Krill herd optimization algorithm is introduced in 2012 to solve the Global Optimization Function. This is a population-based Swarm Intelligence Search Algorithm based on the Herding behavior of krill.   In the Krill herd optimization algorithm, we have a  Group of Krill individuals and they are  Se...

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 [ CAN Design Optimization using Teaching Learning Based Optimization Algorithm] PROBLEM STATEMENT:  Design a Can to Hold 800ml Liquid. OBJECTIVE: Minimize the CAN Manufacturing Cost, Minimize Amount of Sheet Metal Required.  CONSTRAINTS: For Diameter, It should be no greater than 16 cm and no less than 4.0 cm, For Height, It should be no more than 36 cm and no less than 16 cm.                                                     4.0 <= Diameter <= 16; cm                                                     16<= Height <= 36; cm Constraints to HOLD 800ml Liquid Capacity. OBJECTIVE FUNCTION: COST Function Used to Solve this problem: RESULT: AFTER OPTIMIZATION USING TEACHING LEARNING BASED OPTIMIZATION ALGO...

SHARK SMELL OPTIMIXATION ALGORITHM [ Numerical Example ] Shark Smell Optimization Algorithm is population based Metaheuristic optimization algorithm. Shark Smell Optimization Algorithm is inspired by the Shark food foraging behavior.   Shark Smell Optimization Algorithm Steps: Initialize Algorithm Parameters Initialize Population for N Sharks in the search space. Evaluate Performance. While (current Iteration < Maximum Iteration) Calculate Shark Velocity Calculate Shark Position based on forward movement. Calculate Shark Position based on rotational movement. Identify Shark next position based on forward and rotational movements. Evaluate Performance. End While Display Best Solution. STEP 01: Initial Important Parameters.          Current_Iteration =1;          Maximum_Iteration = 10;          and other. STEP 02: Initial Population Randomly. Suppose, Population Size = 2; Position(1) = -0.9891 Positi...