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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Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...
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Grasshopper Optimization Algorithm (G.O.A.)
Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs.
This is the mathematical model used to represent grasshopper behavior in this algorithm :
𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i
GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction.
Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature.
Grasshopper Optimization Algorithm Steps.
1.) Parameter Initialization.
2.) Population Initialization Phase.
3.) Compute Fitness Value for each grasshopper.
4.) Select the Best Solution Among All.
5.) Check While (CurrentIteration (t) < MaximumIteration (MaxT)).
6.) Normalize distance between grasshoppers in the range [1, 4].
7.) Update the position of the current grasshopper.
8.) Bring the Current grasshopper back if it goes outside boundaries.
9.) Update Current Best Solution if there any new Best solution.
10.) CurrentIteration = CurrentIteration + 1; // End While Loop
11.) Return Best Solution.
Grasshopper Optimization Algorithm Advantages.
Obtain better solution as compare to other metaheuristic algorithms
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