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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.

 Black Hole Optimization Algorithm || BHO Algorithm The black hole algorithm is a Natural Heuristic Algorithm by simulating the ‘‘Black Hole’’ phenomenon in the universe.  Black Hole Optimization Algorithm is a new bio-inspired metaheuristic approach based on the observable fact of black hole phenomena. Black Hole : Region in the space where Gravity is so Strong. No object can escape from its powerful gravitational pull. Black holes are formed in the  space when star of massive size collapses. Anything falls into black hole is forever gone from our universe. Black Hole Optimization Algorithm Steps: Step 01: Randomly Initialize population for N candidate solutions in the search space. Consider Stars as initial population and each start is candidate solution. And Best  Candidate among all (i.e., Best Solution) is considered as Black Hole. Step 02: Calculate Fitness Values for each agent in the current population. Calculate fitness values for each candidate and best among all is consider

Battle Royale Optimization Algorithm Learn New optimization algorithm Battle Royale Optimization (BRO) Algorithm step-by-step with Example. Battle Royale Optimization Algorithm Video Chapters: Introduction: 00:00 Battle Royale Optimization Algorithm: 01:17 About Battle Royal: 02:46 Battle Royal Video Games: 03:27 Battle Royal Games Key Components: 04:40 BRO Algorithm: 06:30 Battle Royale Optimization Algorithm Steps: 07:49 Battle Royale Optimization Algorithm Flowchart: 14:44 Conclusion: 16:00 Battle Royale Optimization Algorithm (BROA) is recently proposed Metaheuristic Algorithm. Battle Royale Optimization Algorithm is a Population based Metaheuristic Algorithm that is inspired by GAME. Battle Royale Optimization Algorithm is developed by Taymaz Rahkar Farshi. BRO algorithm is basically inspired by the genre of Digital Games (Battle Royale Video Games). Battle Royal video game is online Multiplayer video game. In 21st century Battle Royale word taken new meaning i.e., "Fictiona

Cat Swarm Optimization Algorithm | CSO Algorithm |  Cat Swarm Optimization Algorithm  Video Chapters: CSO Introduction: 00:00 Cat Swarm Optimization Sub-Models 02:03 CSO Seeking Mode: 03:26 CSO Tracing Mode: 05:19 Cat Swarm Optimization flow chart: 06:56 Cat Swarm Optimization Steps: 08:25 Cat's: 10:48 Cat Swarm Optimization Algorithm is an Metaheuristic Optimization Algorithm. It comes in the category of Nature Inspired Swarm Based Optimization Algorithm. As we all know Nature Inspired Swarm Based Optimization Algorithms are Stochastic methods designed to solve different optimization problem's. Cat Swarm Optimization Algorithm is inspired by behavior of cats in real life. Cat Swarm Optimization Algorithm is invested in 2006 by Shu Chuan Chu. Author tested Cat Swarm Optimization Algorithm using 23 Classical benchmark functions and 10 modern benchmark function.  Two sub-models are used in Cat Swarm Optimization Algorithm to describe cat's behavior. They are modeled on Cats b

  Neuro-fuzzy hybrid system tutorial |Soft Computing| Neuro-Fuzzy Hybrid System (NFHS) - Soft Computing (Neural Network) An introduction to the Neuro-Fuzzy System. In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-Fuzzy Hybrid System is a combination of Neural Network and Fuzzy Logic. Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as the fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are uni