An ant in Ant Colony Optimization algorithm for TSP produces a tour by
1)a deterministic greedy constructive method
2)a stochastic greedy constructive method
3)a deterministic perturbation of the previous tour
4)a stochastic perturbation of the previous tour
Answers
Answer:option b. please follow the text and image
Explanation:
Ant colony optimization or rather ACO is developed on the basis of some observations which are made that sought that the real ants are capable of finding the shortest path from a certain given food source to a specific nest without using visual cues. To explain how does the given real ant colony searches for the shortest path, an example is given as suppose A is the food source and E is the final nest. The objective of the ants is to bring the food back to their very nest. Now it's quite obvious that the shorter paths have advantage compared with the longer ones which are found. Suppose that at time t = 0 there are 30 ants at point B and also 30 at point D. And at this instant there is no trail on any other segments. So the ants will randomly choose their path with all equal
possibility of probability. So, on an average 15 ants from each and every node will go toward H and 15 toward C. At time instant t = 1 the 30 new ants are coming to B from A and thus find a trail of intensity, now 15 on path which leads to H, laid by those 15 ants that went along the way from B, and a trail of 30 on the path shown to C, hence received as the sum of the trail laid by those 15 ants that went that way from B and also by the 15 ants that reached B coming from D via C .The probability of path chosen is found out to be fully biased, so that the expected number of ants going toward C and will be the double of those going toward H that gives us 20 versus 10, respectively for every such nodes related. The same is true for the new 30 ants in D which comes next from E. This process goes on until all of the ants will successfully choose the shortest path.
An ant in the "Ant Colony Optimization algorithm" for "TSP" produces a tour by 2) "a stochastic greedy".
Explanation:
"Ant colony optimization" (ACO) is an exploratory algorithm. It is applied to a number of "combinatorial optimization problems". Moreover, it is implemented by taking into account "high-performance computing methods" for TSP. Thus, the ACO algorithm designed for TSP makes a tour by means of a stochastic greedy constructive manner.