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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...
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Dragonfly Optimization Algorithm Step-by-Step with example

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Dragonfly Optimization Algorithm (DOA)

Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan. 

Dragonfly Algorithm Step-by-Step: -
Step 01: Initialize Dragonfly Population Randomly (𝑋_𝑖, Where i = 1,2,3,4,…n). 
Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖).
Step 03: While(CurrentIteration < MaximumIteration)
Step 04: Computer Fitness Values for each dragonfly.
Step 05: Update Food sources and enemy. 
Step 06: Update parameters w, s, a, c, f, and e.
Step 07: Calculate S, A, C, and F.
Step 08: Update neighboring radius. 
Step 09: If the dragonfly has at least one neighboring dragonfly. { 
   Update Velocity and Position;
} 
else { Update Position; }
Elseif { Check and correct new position based on boundaries of variable; }

Note: To Improve randomness, we can update the dragonfly position using random walk (i.e., Levy’s Flight).




Dragonfly Optimization Algorithm on Different Engineering Design Problems

Engineering Design Problem
Engineering design problems include different complicated Cost Function (aka Fitness Function / Objective Functions). Engineering Optimization Techniques Aim is to “Find out Optimum solution from all feasible solutions”.

How Metaheuristic Algorithms Solve Engineering Design Problems?
Metaheuristic algorithms used randomization process. Metaheuristic algorithms are suitable for global optimization. For difficult engineering problems, develop and utilize metaheuristic algorithms (which may obtain good results).

Engineering Design Problems Example
A dragonfly optimization algorithm is applied to different engineering design problems: 
Welded Beam Design Optimization Problem
Speed reducer design optimization problem
Compression spring design optimization problem

Dragonfly Optimization Algorithm on Different Engineering Design Problems ~xRay Pixy

#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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