To watch the rest of the videos, click here: www.udemy.com/antcolonyoptimi... In this course, you will learn about combinatorial optimization problems and algorithms including the Ant Colony Optimization.
You are a legend , you are a diamond , you are a hero even I stay here for the whole of my life I can't describe how a wonderfull teacher realy you are , thank you so much .
Incredibly well explained. Especially with the visual and mathematical examples. Thank you very much Mister Mirjalili! I took the opportunity to subscribe to some of your udemy course. I hope, later this year, I will have enough time to learn more about this and the other algorithms.
I believe this video is very helpful, expecially for those people approaching ACO for the first time. Having said that, I have a question about ACO. The heuristic information in your example is based on the cost of the single arc. But in many application the cost of a single component is influenced by the value assumed by the other componets. For istance, consider a problem of pump scheduling in water distribution networks, the actuation of a pump in a precise time interval produce a cost that depends on the pump state (on/off) during the preceding time intervals, and it influences the costs of the subsequent time intervals. In this case, how the pheromone trails can be updated? Because the cost function depends on the whole path the ant follows and not by a single component of the path.
Thank you, Ali.. your video is very helpful to finish my task. I didn't know before, this algorithm can be understood in an easy way. Hope to see more of your video. Thank you
I really enjoyed this clearest way to explain things and making super simple and understandable. Thank you so much. this my first sight of your course and i will stay learning here
amazing video!!! very well explained!! Only suggestion, not trying to be rude, but some words at moments can be a tiny bit hard to understand, my suggestion is maybe type of the captions? Other than that great video!
The clearest video on youtube about Ant Colony Optimization, thank you very much Dr. Ali. Just a little correction on [5:05](ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-783ZtAF4j5g.html), the formula for the total pheromone from edge i-j, (T^{i, j}) shouldn't have the K superscript.
This lecture remained very use full. Thank you so much sir. Kindly upload a video, regarding how to program this algorithm (ACO) in any of the programming language like python, IBM LOG cplexx or matlab. Thanks
Sir, thank you for the effective video, and for your effort, can you please explain to us the Artificial Bee Colony optimization, sir can I use the ABC algorithm to solve the shortest path problem? another question please, what are the meta-heuristics methods used to solve the shortest path problem? waiting for your response thank you in advance.
Reminds me on Markov chains. Hard part is that every time concentration of feromon is different so probability changes. If also quality of road change I wonder how to calculate stationary distribution if there is any at all.
you have upload your code inmatlab . i ma facing difficulty in running that code in matlab. can you help me in that . i tink i am facing a problem i calling function into other function in matlab. can you give some advise
guys please when we are talking about I,j, Is the current ant position and j is one of the next steps around it like the left to right ? or j is the final destination like nest or car and tree?
If my problem is a combinatorial problem, but I can only calculate the cost once a full trail has been formed, how can I calculate the distance between two nodes? Or should I just ignore that part?
I have a question concerning evaporation rate - Why is time not a variable in the equation for pheromone levels? Additionally, how can one include more environmental variables such as temperature, terrain, humidity, light, etc. or colony variables such as speed of ants, density, distribution, type of ant, etc. into the algorithm? Is there any other type of communication that occurs between individuals?
Wanna know whether the pheromone is updated locally on each arc visited by ant or the pheromone is updated globally after the ants visits all the arcs in a complete path....?
Shouldn't the kth tau equal the old tau plus the change in tau, if there is no vaporization? The way it is written now (6:10) means rho = 1, which equals maximum vaporization. Or am I misunderstanding something?
shouldn't the evaporation be subtracted rather than added to current pheromone level? at 10:44 the pheromone level has increased from 0.1 to 0.6 with evaporation.
If you intend to use ACO for model optimization then you need to implement each model as an ant & use the idea of pheromones either as penalty or dependent on your desired parameters. By doing so, you essentially put the models as ensemble & are optimizing the ensemble by favoring the best model. Having said that, it's not essential to implement ACO for optimization of predictive model control courtesy of requirements of high processing cost & nearly chance of not giving the correct output (ACO is notorious for not always giving best results). Thus, i suggest looking at other optimization methods or look into cost/error functions in terms of penalty for ensemble methods.
Ali, I've watched many RU-vid videos and yours is without a doubt the best explanation. I'm working on a small project using your algorithm to develope an app for iOS. At 9:21, what if there is a path that hasn't been taken yet? That path would have a Pheromone value of 0? Even with the evaporation model the value would be 0. How do you consider this path to be taken? Thank you.
Hi Ali , First of all thank you for this amazing video and , my question is , how we can choose the destination city from a bunch of cities with equal probability values?
There are different models. Some, chose the closest city. Others, simply don't care. The idea is for the simulation to, eventually, show you the optimum path. So even when two have the same probability for a given time, in the long term, the one which favors the best path will end up with a higher value. If two paths are equally optimum, then your model - having same probabilities - will reveal it.
at 6:44 in the video, you say if roh is equal to 1 the evaporation is at its maximum, but mathematically if roh is equal to 1 the first term of second equation will become zero and we will be left with the equation which is equal to pheromone level without evaporation. so what's going on here?
I assume they would normally originate from a nest, but in reality there will of course be situations where scouts take random routes that eventually become solid paths. That's why most ant lines aren't the perfect shortest path from source to nest, but are shorter relative to routes taken by other scouts.
