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
Butterfly Features: Small Head, 2 compound eyes. Butterfly basically feed on Nectar from flowers. Adult Butterfly consume only liquid [nectar from flowers]. They use their Antenna to sense air from wind and fragrance. Butterfly can fly only when their temperature is 27℃ or 81℉. Largest butterfly in the world: Queen Alexandra Birdwing. Butterflies also derive nourishment from rotting fruits, dung, decaying flesh, dissolved minerals in the dirt/ sand.
Butterfly Lifecycle
An Adult Butterfly lay eggs on the food plant. From Eggs to Larva [Larva consume plant leaves].
When metamorphosis complete. Puppet skin splits and adult insect climb out. When wings dry it can fly.
Butterfly Optimization Algorithm (B.O.A.)
Butterfly Optimization Algorithm (B.O.A.) is a latest Nature-Inspired Population Based Algorithm. Butterfly Optimization Algorithm is basically inspired by the foraging and mating behavior of butterflies. Butterfly Optimization Algorithm (B.O.A.) is basically inspired by various foraging strategies of butterfly. Butterflies use their sense of Smell, Sight, Taste, Touch, and Hearing.
In Butterfly Optimization Algorithm Butterfly are Search Agents. In this algorithm, butterfly will generate fragrance [i.e., Fitness Values]. As butterfly move from one location to another, their fragrance/fitness values change accordingly. Butterfly can sense the fragrance. When any butterfly is able to sense the fragrance from other butterfly it moves toward it [I.e., Global Search]. When any butterfly is not able to sense the fragrance from the surrounding, then it will move randomly [i.e., Local Search].
In Butterfly Optimization AlgorithmButterfly MOVEMENT
1.) All butterflies are suppose to emit some fragrance. Which enable the butterflies to attract each other.
2.) Butterflies will move Randomly or toward the best butterfly [emitting more fragrance].
Butterfly Optimization Algorithm Pseudocode
Objective function f(x).
Generate Initial Population of n Butterflies.
Calculate Fitness values / Intensity Values for each butterfly.
Define sensor modality c, power exponent a and switch probability p.
While Check stopping criteria
For Each butterfly in the population
Calculate Fragrance.
End For
Find out Best Butterfly among all.
For Each butterfly in the population
Generate a random number r from [0,1].
Check if(r < p)
Move towards best butterfly / Solution.
Else
Move butterfly randomly.
End if
End For
Update the value of a
End While
Print best solution obtained
How to Calculate Fragrance for each butterfly?
How to Move towards best butterfly / Solution?
How to Move butterfly randomly in the search space.
PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n 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. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2] 1.) Initialize Population [Current Iteration (t) = 0] Population Size = 4; 𝑥𝑖 : (i = 1,2,3,4) and (t = 0) 𝑥1 =1.3; 𝑥2=4.3; 𝑥3=0.4; 𝑥4=−1.2 2.) Fitness Function used:
Cuckoo Search Algorithm - Metaheuristic Optimization Algorithm What is Cuckoo Search Algorithm? Cuckoo Search Algorithm is a Meta-Heuristic Algorithm. Cuckoo Search Algorithm is inspired by some Cuckoo species laying their eggs in the nest of other species of birds. In this algorithm, we have 2 bird Species. 1.) Cuckoo birds 2.) Host Birds (Other Species) What if Host Bird discovered cuckoo eggs? Cuckoo eggs can be found by Host Bird. Host bird discovers cuckoos egg with Probability of discovery of alien eggs. If Host Bird Discovered Cuckoo Bird Eggs. The host bird can throw the egg away. Abandon the nest and build a completely new nest. Mathematically, Each egg represent a solution and it is stored in the host bird nest. In this algorithm Artificial Cuckoo Birds are used. Artificial Cuckoo can lay one egg at a time. We will replace New and better solutions with less fit solutions. It means eggs that are more similar to host bird has opportunity to develop in the new generation a
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
Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n 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. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes the op
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? A. Wave or vibration. In th
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
Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 1.) Initialize Grey Wolf Population. 2.) Initialize a, A, and C. 3.) Calculate the fitness of each search agent. 4.) 𝑿_𝜶 = best search agent 5.) 𝑿_𝜷 = second-best search agent 6.) 𝑿_𝜹 = third best search agent. 7.) while (t<Max number of iteration) 8.) For each search agent update the position of the current search agent by the above equations end for 9.) update a, A, and C 10.) Calculate the fitness of all search agents. 11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹 12.) t = t+1 end while 13.) return 𝑿_𝜶 Grey Wolf Optimization Algorithm Numerical Example STEP 1. Initialize the Grey wolf Population [Initial Position for each Search Agent] 𝒙_(𝒊 ) (i = 1,2,3,…n) n = 6 // Number of Search Agents [ -100, 100] // Range Initial Wolf Position 3.2228 4.1553 -3.8197 4.2330 1.3554 -4.1212 STEP 2. Calculate Fitness for Each Search Agent. Objective Function: F6(x) = su
Whale Optimization Algorithm Code Implementation Whale Optimization Algorithm Code Files function obj_fun(test_fun) switch test_fun case 'F1' x = -100:2:100; y=x; case 'F2' x = -10:2:10; y=x; end end function [LB,UB,D,FitFun]=test_fun_info(C) switch C case 'F1' FitFun = @F1; LB = -100; UB = 100; D = 30; case 'F2' FitFun = @F2; LB = -10; UB = 10; D = 30; end % F1 Test Function function r = F1(x) r = sum(x.^2); end % F2 Test Function function r = F2(x) r = sum(abs(x))+prod(abs(x)); end end function Position = initialize(Pop_Size,D,UB,LB) SS_Bounds = size(UB,2); if SS_Bounds == 1 Position = rand(Pop_Size,D).*(UB-LB)+LB; end if SS_Bounds>1 for i = 1:D UB_i = UB(i); LB_i = LB(i); Position(:,i) = rand(Pop_Size,1).*(UB_i-LB_i)+LB_i; end end end function [Best_Val,Best_Pos,Convergence_Curve]=WOA(
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