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
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Harris Hawks Optimization Algorithm Step-by-Step with Example ~xRay Pixy🐇🌿🌞
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Harris Hawks Optimization Algorithm
Harris Hawks Optimization Algorithm Introduction
Harris hawks optimizer (HHO) is a nature-inspired population-based optimization algorithm. Harris Hawks Optimization algorithm is a Population-based algorithm. This algorithm mimics the Exploring, Exploiting, and Attacking strategies of Harris Hawks. Harris Hawks Optimization algorithm can be used to solve different engineering problems.
About Harris Hawk
Harris Hawk is a large medium bird and also known as Dusky Hawk. Harris Hawk hunt in cooperative groups and group size is 2 to 7 birds. Hawks Diet: Large insects, Birds, Lizards, and Mammals.
Harris Hawks Optimization Algorithm Steps-by-Step.INPUT: Population Size (N), and a maximum number of iterations (MaxT).
OUTPUT: Target Location and its Fitness Values.
Step 01: Initialize the population randomly 𝑋_𝑖 ( 𝑖=1,2,3,4,…𝑁 ).
Step 02: Check While ( (t ≥ max )Stopping Criteria is Matched or Not )
Step 03: Calculate fitness value for each hawk and Select Best.
Step 04: Set 𝑋_𝑟𝑎𝑏𝑏𝑖𝑡 as the best location for the rabbit.
Step 05: For each hawk Position: update Energy and Jump Strength.
Step 06: Exploration Phase
Step 07: Exploitation Phase
Following 2 Strategies are Followed by Hawks to detect a prey:
Step 08: Update Location Vector using 4 strategies: Soft Round-Up, Hard Round-Up, Soft round-up with progressive rapid dives, Hard round-up with progressive rapid dives.
Step 09: Return Best Rabbit Location and its Fitness Value.
How Harris Hawks Detect Prey?
Harris Hawks haunt randomly on certain cities and wait to detect prey. They check the Position of other family members and target position.
Q. How hawks will catch the prey?CASE 01: Soft round Up ( E ≥ 0.5 and r ≥ 0.5 )
CASE 02: Hard round Up ( E < 0.5 and r ≥ 0.5 )
CASE 03: Soft Round-Up with progressive rapid dives. ( E ≥ 0.5 and r < 0.5 )
CASE 04: Hard round Up with progressive rapid dives. ( E < 0.5 and r < 0.5 )
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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(
can you suggest or share document for the HHO mathematical equation explanation? thank you.
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