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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

 Introduction to Metaheuristic Optimization Metaheuristic represents the family of approximate Optimization Techniques / Algorithms. Using Metaheuristic Techniques we can solve complex real world problems, Engineering design problems, Scientific Problems, Industrial Problems and obtain acceptable solution within time [ especially used to solve Science and Engineering Problem s]. In Metaheuristic , META means Upper Level Methodology / High Level Procedure and HEURISTIC means Art of discovering new strategies or rules to solve problems / discover or learn something for themselves.  Metaheuristic is an advanced technique used to locate, create, or select a heuristic that may provide correct and acceptable results to Optimization Problems. Optimization is finding better solutions among different possible solutions within acceptable amounts of time. Metaheuristic algorithms are best to solve complex engineering design problems. Popular Metaheuristic Algorithms are:  Genetic Algorithm (G

Mayfly Optimization Algorithm Learn Mayfly Optimization Algorithm Step-by-Step with Example. Mayfly optimization algorithm is inspired by mayfly flight behavior and mating process. We can use this algorithm to solve single objective optimization problems and multi objective optimization problems. Mayfly Optimization Algorithm Video Chapters: Introduction: 00:00 Mayfly behavior, Life Cycle: 01:27 Single Objective Mayfly Optimization Algorithm: 03:25 Mayfly Optimization Algorithm Steps: 04:35 Update Mayflies Position and Velocity: 06:25 Mate the Mayflies: 11:27 Conclusion: 13:43 Mayfly optimization Algorithm is developed by Zervoudakis K. and Dr. Tsafarakis S. Mayfly optimization Algorithm is inspired by the flight behavior and mating process of mayflies. Mayfly optimization Algorithm is modification of Particle Swarm Optimization (P.S.O). It use the key advantages of Swarm Intelligence Algorithms and Evolutionary Algorithms and form Hybrid Algorithmic Structure . Mayfly optimiz