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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

 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

Emperor Penguin Optimizer   Learn Emperor Penguin Optimization Algorithm Step-by-Step. A bio-inspired algorithm which mimic the huddling behavior of Emperor Penguin. Video Chapters: Introduction: 00:00 What is emperor penguin optimization: 00:23 Emperor Penguin: 00:58 Emperor Penguin huddle: 01:50 Emperor Penguin Optimizer Flowchart: 03:19 Generate Emperor Penguin huddle boundary: 04:34 Calculate Temperature around huddle: 06:18 Calculate Distance between Emperor Penguins: 07:45 Relocate Effective Mover: 09:45 Emperor Penguin Optimization Steps: 10:52 Conclusion: 12:38 Emperor Penguin Optimizer is a Novel Bio-Inspired Metaheuristic Algorithm which is inspired by the huddling behavior of Emperor Penguin. Penguins are Aquatic Flightless Birds. Penguins spends their 50% life on the land and 50% life in the water. Penguin largest species is known as Emperor Penguin. Both male and female emperor penguins are similar in size. Emperor penguin is the only species which use huddle for their

Random Number Generator |Mid Square Method|   Algorithm: Step 1: Stating with n (4 digits long) Digit Number.   Step 2: Squaring it. Step 3: For 8 digit: Remove 2 lower and 2 higher order DigitStep 4. For 7 digit: Remove 1 higher-order digit & 2 lower order.Step 5. Repeat step 2. Mistake :   F or  7 digit:  Remove 1  higher-order  digit & 2  lower order. Another Example This method  will either begin repeatedly generating the same number or  Cycle  to a previous number in the sequence and loop indefinitely

OPTIMIZATION ENGINEERING Optimization: In Optimization we either minimize or maximize objective functions / cost function. No Free Lunch Theorem for Optimization No Free Lunch Theorem for Optimization According to No Free Lunch Theorem "There is no universal better algorithm exist that can solve all types of optimization problems". Today, Metaheuristic Optimization Algorithms are used in different areas to solve complex real work optimization problems. For example in Industrial Areas, Operation Research, Medical Field, Engineering design and other as you can see below:  History of Metaheuristic Optimization Algorithms: Genetic Algorithms (G.A.) - 1960's - 1970's Simulated Annealing (S.A.) - 1983 Tabu Search (T.S.) - 1986 Ant Colony Optimization Algorithm - 1992 Particle Swarm Optimization Algorithm - 1995 Differential Evolution (D.E.) -1997 Harmony Search (H.S.) - 2001 Honey Bee Algorithm (H.B.A.) - 2004 Artificial Bee Colony (A.B.C.) - 2005 ... Battle Royal Optimizat

No Free Lunch Theorem for Optimization No Free Lunch Theorem for Optimization According to No Free Lunch Theorem "There is no universal better algorithm exist that can solve all types of optimization problems". Today, Metaheuristic Optimization Algorithms are used in different areas to solve complex real work optimization problems. For example in Industrial Areas, Operation Research, Medical Field, Engineering design and other as you can see below:  History of Metaheuristic Optimization Algorithms: Genetic Algorithms (G.A.) - 1960's - 1970's Simulated Annealing (S.A.) - 1983 Tabu Search (T.S.) - 1986 Ant Colony Optimization Algorithm - 1992 Particle Swarm Optimization Algorithm - 1995 Differential Evolution (D.E.) -1997 Harmony Search (H.S.) - 2001 Honey Bee Algorithm (H.B.A.) - 2004 Artificial Bee Colony (A.B.C.) - 2005 ... Battle Royal Optimization Algorithm (B.R.O.A.) - 2020 In 1997, D.H. Wolpher and W. G. Macready published No Free Lunch Theorem for optimization.