| 1 -Ant Colony Optimization for TSP.mp4 | 110.1 MB | ||
| 1 -Artificial Bee Colony.mp4 | 31.2 MB | ||
| 1 -Grey Wolf Optimizer.mp4 | 9.4 MB | ||
| 1 -Intro.mp4 | 15.1 MB | ||
| 1 -Introduction.mp4 | 15.7 MB | ||
| 1 -Project Introduction.mp4 | 15.3 MB | ||
| 1 -Tabu Search for TSP.mp4 | 80.4 MB | ||
| 1 -Theory & Scenario.mp4 | 16.8 MB | ||
| 1 -Theory and Scenario.mp4 | 40.1 MB | ||
| 1 -What is Genetic Algorithm.mp4 | 14.1 MB | ||
| 1 -What is SPEA2.mp4 | 14.5 MB | ||
| 2 -Code.mp4 | 38.7 MB | ||
| 2 -Model.mp4 | 16.4 MB | ||
| 2 -Project.mp4 | 8.4 MB | ||
| 2 -Terms.mp4 | 15.4 MB | ||
| 2 -Theory.mp4 | 29.9 MB | ||
| 2 -Whale Optimization Algorithm.mp4 | 10.1 MB | ||
| 3 -Case Study.mp4 | 11.7 MB | ||
| 3 -Code and Output.mp4 | 31.2 MB | ||
| 3 -Code.mp4 | 29.3 MB | ||
| 3 -Coding.mp4 | 53.6 MB | ||
| 3 -Model of SPEA2's.mp4 | 15.7 MB | ||
| 3 -Output.mp4 | 7.7 MB | ||
| 3 -Project.mp4 | 7.1 MB | ||
| 3 -Rastrigin Function.mp4 | 12.2 MB | ||
| 3 -Scenario.mp4 | 9.4 MB | ||
| 4 -Let's Code.mp4 | 56.1 MB | ||
| 4 -Math Model.mp4 | 21.5 MB | ||
| 4 -Mathematical Model.mp4 | 14.3 MB | ||
| 4 -Model of AFSA.mp4 | 10.2 MB | ||
| 4 -Model.mp4 | 12.5 MB | ||
| 4 -Output.mp4 | 8.8 MB | ||
| 4 -Outputs and Graph.mp4 | 18.5 MB | ||
| 5 -Code Time.mp4 | 41.2 MB | ||
| 5 -Code of AFSA.mp4 | 15.5 MB | ||
| 5 -Code.mp4 | 25.6 MB | ||
| 5 -Coding with DEAP.mp4 | 65 MB | ||
| 5 -Review of Results.mp4 | 35.4 MB | ||
| 6 -DEAP's Results.mp4 | 27.4 MB | ||
| 6 -Outcomes of AFSA.mp4 | 4.2 MB | ||
| 6 -Outputs.mp4 | 8.2 MB | ||
| 6 -Results.mp4 | 3.7 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 58 total files | |||
Metaheuristic & Heuristic Optimization in Python
https://WebToolTip.com
Published 8/2025
Created by Advancedor Academy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 56 Lectures ( 3h 15m ) | Size: 1.37 GB
Solve complex optimization problems using Genetic Algorithms, Swarm Intelligence, A*, and Simulated Annealing in Python
What you'll learn
Apply heuristic and metaheuristic optimization algorithms such as GA, PSO, A*, and Simulated Annealing to real-world problems.
Build and analyze mathematical models for complex optimization tasks using Python.
Implement optimization algorithms from scratch or using libraries like DEAP, PyGAD, and Scikit-Opt.
Evaluate algorithm performance and compare solution quality across different techniques.
Requirements
Basic Python programming knowledge (variables, loops, functions).
Familiarity with basic optimization or operations research concepts is helpful but not required.
No prior experience with metaheuristics is needed — everything will be taught from the ground up.
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