The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā Strong branches grow ...
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Learn How to Apply Ant Colony Optimization to Traveling Salesman Problem...
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Ant Colony Optimization Metaheuristics for the Traveling Salesman Problem Numerical Example
In this video you will learn, How to Solve Traveling Salesman Problem (TSP) using Ant Colony Optimization Algorithm (ACO).
Ant System for the Traveling Salesman Problem Chapters:
Introduction : 00:00
Traveling Salesman Problem (TSP): 00:33
Ant Colony Optimization Traveling Salesman Problem Example: 02:11
Distance Calculation between Cities: 04:30
Solution Construction for Artificial Ants: 06:45
Ants Tour Comparison for TSP: 14:52
Conclusion: 15:45
Part 1: Learn Ant Colony Optimization Algorithm
Ant Colony Optimization Algorithm step-by-step with Example (ACO) ~xRay Pixy
Ant System for the Traveling Salesman Problem. Ant Colony Optimization is a Nature Inspired Approach. Ant Colony Optimization (ACO) algorithm is basically inspired by the foraging behavior of ants searching for suitable paths between their colonies and food source. Ant Colony Optimization (ACO) algorithm is inspired by the social behavior of real ant colonies. Ant colony optimization algorithm is developed by Marco Dorigo 1992. Ant Colony Optimization has used for Routing and load balancing problems. Ant colony optimization can be applied to continuous optimization problems.
Ant Colony Optimization is a stochastic technique used for solving Computational problems [e.g., Finding an optimal Path / Finding shortest path through Graph]. Author developed this algorithm to solve Discrete Variable Combinatorial Optimization Problems. Today, It is also used to solve Continuous variables and other problems as well. Ant Colony Optimization Terminology: Pheromones, Pheromone Trials, Pheromone Density and Pheromone Evaporation.
Optimization Problems can be solved using Ant Colony Optimization (A.C.O.): Applications of Ant Colony Optimization Algorithms
Continuous and Discrete Optimization Problems.
Telecommunication Problems.
Routing Problems.
Load Balancing Problems.
Traveling Salesman Problem.
Vehicle Routing Problem.
Scheduling Problems.
Assignment Problems.
Subset Problems.
Networking Problems.
How Ant Colony Optimization is applied to solve Traveling Salesman Problem?
Traveling Salesman Problem: In traveling salesman problem, salesman want to visit a number of cities and cover minimum distance during tour.
Constraint: Each city should be visited exactly once. [to minimize tour length]
Ant Colony Optimization is applied on Traveling Salesman Problem to solve this problem. A number of Artificial Ants are used. We will use Artificial ants to visit all cities and calculate tour length for each ant. Out of (n) ants, check feasible solution i.e., feasible tour with minimum tour length.
Ant Goal: Find out Feasible Tour for the salesman problem.
Objective function Value: Sum of distance between each city visited during tour.
Ant Colony Optimization Metaheuristics for TSP Steps
Step 01: Construct Graph for the given problem. Each city is considered as graph NODES / VERTICES and distance between cities are graph EDGES.
Step 02: Initialize all important parameters, Number of Artificial Ants, Maximum Iterations, Artificial Pheromones and other.
Step 03: For any Ant select ant city randomly. Place that ant in randomly Selected city.
Step 04: Build the Tour for Ant from randomly selected city to unvisited cities. Ants are also using artificial memory to store visited city.
Step 05: One by one move ant to all the unvisited cities and calculate tour length.
Step 06: Once no unvisited city left. Ant will return to the randomly selected city. Then calculate Total Tour Length. Repeat this for all ants.
Step 07: Update Artificial Pheromones Values.
Step 08: Check stopping criteria. If stopping criteria is not matched repeat loop ELSE display the best solution.
For More Details and Numerical Example you can visit this video.
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