Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion
Learn the Horse herd optimization Algorithm (HOA) Step-by-Step.
- Nature Inspired Metaheuristic Optimization Algorithm
- Inspired by Horse Herd Behavior.
- A large number of Controlling Parameters are Used.
- Used to Solve Higher Dimensional Optimization Problems in real life.
A horse herd optimization algorithm is introduced in 2021. It is the nature-inspired population-based metaheuristic optimization algorithm that is basically inspired by the horse herding behavior in nature.
Disadvantage: Due to horse different behaviors, a large number of control parameters are used in this algorithm.
Advantage: We can use this algorithm to solve complex high-dimensional optimization problems.
Few Facts About Horses
Horse Lifespan: 15-30
Vision: 360 Degree
Brain Weight: 623 Gram
Male Horse Teeth: 40
Female Horse Teeth: 36
Horse Breeds: More than 300
Predators: Lion, Wolf, and other
Horses Behavior: Horses reveal different behavior at different ages. Different horses behavior used in this algorithm include:
Grazing (G)
Hierarchy (H)
Sociability (S)
Imitation (I)
Roam (R)
Defense Mechanism (D)
Horse Age Classification
Delta Horse: Age Range Between 0-5 Years.
Gama Horse: Age Range Between 5-10 Years.
Beta Horse: Age Range Between 10-15 Years.
Alpha Horse: Older than15 years.
Horse Herd Optimization Algorithm Pseudocode:
Initialize important algorithm parameters.
Initialize Population for N horses Randomly.
Calculate the Fitness value for each horse in the current population.
Select the best horse among all.
Determine horse age: Calculate Alpha, Beta, Gama, and Delta Horse.
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Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm. In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to upda
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Grey Wolf Optimization Algorithm (GWO) Grey Wolf Optimization Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves. What is Metaheuristic? Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization. Who are the Grey Wolves? Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild). Scientific Name: Canis Lupus. Family: Canidae (Biological family of dog-like carnivorans). Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12. 4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf. How Grey Wolf Optimization Algorithm
Local Binary Pattern Introduction to Local Binary Pattern (LBP) Q. What is Digital Image? A. Digital images are collections of pixels or numbers ( range from 0 to 255). Q. What is Pixel? A. Pixel is the smallest element of any digital image. Pixel can be categorized as Dark Pixel and Bright Pixel. Dark pixels contain low pixel values and bright pixels contain high pixel values. Q. Explain Local Binary Pattern (LBP)? A. Local binary pattern is a popular technique used for image processing. We can use the local binary pattern for face detection and face recognition. Q. What is LBP Operator? A. LBP operator is an image operator. We can transform images into arrays using the LBP operator. Q. How LBP values are computed? A. LBP works in 3x3 (it contain a 9-pixel value ). Local binary pattern looks at nine pixels at a time. Using each 3x3 window in the digital image, we can extract an LBP code. Q. How to Obtain LBP operator value? A. LBP operator values can be obtained by using the simp
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