Learn Markov Chains step-by-step using real-life examples. Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion
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Multiverse Optimization Algorithm Example Step-by-Step Explanation
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Multiverse Optimization Algorithm
Q. What is Multiverse Theory?
A. Multiverse means the group of multiple universes. According to Multiverse theory, there is more than one Big Bang, and each Big Bang causes the birth of a universe. Multiple universes collide and interact with each other.
Q. What is Big Bang Theory?
A. Our universe comes into existence (13 Billion years Ago) from a single, hot and dense point. According to the Big Bang theory, our universe starts with a massive explosion. Before this explosion, nothing exists in this world. The universe contains time, energy, planets, stars, galaxies, and matter.
Q. What is Multiverse Optimization Algorithm?
A. Multiverse Optimization Algorithm (MVO) is inspired by multiverse theory. Multiverse optimization algorithm is basically inspired by three main concepts of multiverse theory: White Hole, Black Hole, and Worm Hole. According to multi-verse theory, white holes / Big Bangs are created where collisions between parallel universes occurred. A black Hole can attract everything (including light) with its gravitational force. Wormhole act as a travel tunnel where objects can travel from one universe to another.
Q. State certain rules that are applied to the universe of MVO.
A. Certain rules are followed in the multiverse optimization algorithm during optimization process:
The Higher the inflation rate, the higher the probability of having a white hole.
The Higher the inflation rate, the lower the probability of having a black hole.
Universes with a higher inflation rate tend to send objects through white holes.
Universes with a lower inflation rate tend to receive more objects through black holes.
The objects in all universes may face random movement towards the best universe via wormholes regardless of the inflation rate.
Q. How universes exchange objects in MVO?
A. Wormholes act as a tunnel in between the white holes and black holes. Assume that wormhole tunnels are always established between a universe and the best universe. (to provide local changes to each universe). The mathematical model is:
Q. Multiverse Optimization Algorithm Step-by-Step
A. Input: Population Size and Maximum Number of iterations.
Output: Display Best Universe and its Fitness Value / Inflation rate.
Step 01: Parameter Initialization.
Step 02: Compute Fitness Value for Each Universe and Select Best Universe.
Step 03: For each universe update WEP and TDR.
Step 04: Select one universe among N by roulette wheel selection mechanisms as a white hole.
Step 05: Use a wormhole as a tunnel for object exchange between different universes.
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There are about 1000 species of Bats. Bat Algorithm is based on the echolocation behavior of Micro Bats with varying pulse rates of emission and loudness. All bats use echolocation to sense distance and background barriers. Microbats are small to medium-sized flying mammals. Micro Bats used a Sonar that is known as Echolocation to detect their prey. Bats fly randomly with the velocity at the position with a fixed frequency and loudness for prey. Q. Whats is Frequency? A. Frequency is the number of waves that pass a fixed point in unit time. Wavelength is the minimum distance between two nearest particles which are in the same phase. Here, Sound waves are used by microbats to detect prey. Q. What is Position? A. A place where something or someone is located. Q. What is Velocity? A. Speed of something in a given direction. Q. What is loudness. A. Loudness refers to how soft or loud sound seems to listeners. Q. What is pulse rate? ...
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