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 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  Searching for Food.  Krill Herd Optimization Algorith

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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 ALGORITHM Optimal Diameter 4.3679 Optimal Height 34.0069 Best Cost = 207.24 Teaching Learning Based Optimization Algorithm  |  TLBO Numerical Example  | Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example. Teaching Learning Based Optimizati

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 Position(2) = -8.3236 STEP 03: Using Fitness Function Calculate Fitness Values. Fitness(1)

Teaching Learning Based Optimization Algorithm  | TLBO Numerical Example | Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example. Teaching Learning Based Optimization Algorithm is based on the effect of Teacher on the Learners in the class. Teaching Learning Based Optimization Algorithm is basically inspired by the behavior of learners in the classroom. In Teaching Learning Based Optimization Algorithm 2 main procedure are followed: Teaching Phase: Learners study from the Teacher. Learner Phase: Learners can interact with each other and they can randomly interact with each other. Teaching Learning Based Optimization Algorithm Steps: Population Initialization Phase. Agents Performance Evaluation using Cost Function. Select Best Solution Among all. Calculate Mean Value. Teacher Phase. Mathematical Model Learner Phase MathematicalModel Evaluate New Solutions. Update Current Best Solution. Check Stopping Criteria. Display Best Solution.

Bacterial Foraging Optimization Algorithm   |PART 2 - NUMERICAL EXAMPLE | _______________________________________________________________________________ [ PART 1 ] Learn Bacterial Foraging Optimization Algorithm (BFOA) Step-by-Step Learning ~xRay Pixy https://youtu.be/LXInp4wvpXM

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 Flower Pollination Algorithm (FPA) Flower Pollination Algorithm (FPA)  is a Nature Inspired Metaheuristic Optimization Algorithm. It is introduced by Xin She Yang in 2012. Flower Pollination Algorithm is a population based metaheuristic algorithm that is basically inspired by the plants  flowering  behavior in nature. Flower Pollination Algorithm outperform different Metaheuristics and provide better results in different fields such as: For Feature Selection. For Image Processing. For Signal Processing. In Computer Gaming. For Wireless Sensor Network Problems. For Structural Design Problems. For Clustering Problems. For Global Optimization Problems. There are more than 250000 species of flowering plants around the world and 200000 species of pollinators. Pollinators play major role in the pollination process. 35% of our food pants are animal pollinated.  Pollen: Pollen are the grains / Yellow Dust like particles.  Pollinators: Pollinators play major role in the pollination process. Po

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 t

Metaheuristic Optimization in Software Testing Video Chapters: Introduction: 00:00 Software Development Life Cycle: 01:38 Software Testing: 03:15 Software Testing Objectives: 05:08 Test Cases in Software Testing: 07:30 Software Testing Process: 08:23 Test Suites in Software Testing: 09:04 Design Test Cases for Software Testing: 09:37 Approaches to Design White Box Test Cases: 14:22 Software Testing Tools: 16:08 Search Based Software Testing: 16:25 Metaheuristic Optimization in Software Testing: 17:33 Software Testing Issues: 18:22 Test Case Prioritization Techniques: 19:51 Solve Test Suite Prioritization Problem using Grey Wolf Optimization: 21:22 Conclusion: 27:50 Software Development Life Cycle Software Development Life Cycle is pictorial representation of Software Development. Software Development Life Cycle (SDLC) is the time period when any software is Created and Ends Software Development Life Cycle Phases: 1.) Requirement Phase: This phase is also known as Requirement Analysis o

Q. Where we can use Metaheuristic Optimization Algorithms? A. Metaheuristic Algorithms are used in different fields to solve optimization problems in different fields either for minimization of for maximization.  Metaheuristic Algorithms Categories.  1. Single Based Metaheuristic Algorithms: Single Solution is generated at each iteration/generation. Single Based Metaheuristic Algorithms Examples. Tabu Search Guided Local Search Iterated Local Search Variable Neighborhood Search Greedy Randomized Adaptive Search 2. Population-Based Metaheuristic Algorithms: Multiple Solutions are generated at each iteration/generation. Examples: Nature-Inspired Metaheuristic Algorithm      Evolutionary Algorithms Swarm Based Algorithm Human-Based Algorithm  Physics-Based Algorithm Bio-Inspired Algorithm Art-Inspired Algorithm Plant-Based Algorithm Q. How we can use Metaheuristic Optimization Algorithms in Software Testing? Metaheuristic Optimization Algorithm in Software Testing: T = {T(1), T(2),..., T