simulated-annealing

问题 Quoting Wikipedia article about simulated annealing: It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). As far as I'm aware the R version of simulated annealing is provided by the package GenSA , https://cran.r-project.org/web/packages/GenSA/GenSA.pdf . And yet I can find nothing in the linked docu about discrete parameters; the docs seem to assume continuous parameters (as there is no way to pass step!) Is there any way to use simulated

问题 Quoting Wikipedia article about simulated annealing: It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). As far as I'm aware the R version of simulated annealing is provided by the package GenSA , https://cran.r-project.org/web/packages/GenSA/GenSA.pdf . And yet I can find nothing in the linked docu about discrete parameters; the docs seem to assume continuous parameters (as there is no way to pass step!) Is there any way to use simulated

问题 I'm looking to implement the simulated annealing algorithm in Java to find an optimal route for the Travelling Salesman Problem, so far I have implemented brute force and am looking to modify that code in order to use simulated annealing. Obviously brute-force and simulated annealing are very different and use very different functions. I understand simulated annealing uses a variable known as the temperature which then cools as the algorithm runs; with the temperature starting high and it

问题 I am trying to use the scipy.optimize package to optimize a discrete optimization problem (global optimization). Acc to the doc, simulated annealing implemented in scipy.optimize.anneal should be a good choice for the same. But I am not sure how to force the optimizer to search only integer values of the search space. Can someone help? An illustrative example: f(x1,x2) = (1-0.4*x1)^2 + 100*(0.6*x2 -0.4*x1^2)^2 where, $x1, x2 \in I$ 回答1: I've checked scipy.optimize.anneal, and I can't see a

问题 I would like to use Simulated Annealing to find local minimum of single variable Polynomial function, within some predefined interval. I would also like to try and find Global minimum of Quadratic function. Derivative-free algorithm such as this is not the best way to tackle the problem, so this is only for study purposes. While the algorithm itself is pretty straight-forward, i am not sure how to efficiently select neighbor in single or n-dimensional space. Lets say that i am looking for

问题 I am trying to come up with the algorithm for a Graph Coloring problem using Simulated Annealing. There is the general algorithm online, but when i look at it, I couldn't understand how can apply this algorithm on this problem. Each node in graph must had diffrent color from it's neibours. How can I use the Simulated annealing algorithm for this. What is the "temperature", "schedule" in this problem? Please help me understand this. Thanks 回答1: Setting the starting temperature and cooling

问题 I am currently trying to use Simulated Annealing package GenSA in order to minimize the function below : efficientFunction <- function(v) { t(v) %*% Cov_Mat %*% v } Where Cov_Mat is a covariance matrix obtained from 4 assets and v is a weight vector of dimension 4. I'm trying to solve the Markowitz asset allocation approach this way and I would like to know how I could introduce mathematical constraint such as the sum of all coefficients have to equal 1 : sum(v) = 1 Moreover since I intend to

问题 I have 7 values and I need to split them into 5 groups. Each group should contain atleast one value. There are 15 ways to group those values into 5. Mon- 13 Tue- 5 Wed- 4 Thu- 4 Fri- 11 Sat- 2 Sun- 1 When grouping, ordering of Mon, Tue, Wed, Thu, Fri, Sat, Sun should be preserved. Suppose there is a function which decides how good a grouping is. 13, 5, 4, 4, 11, 2, 1 Function Grouping 1 - 13, 5, [4,4], 11, [2,1] 13 added, 13 withdrawn, 0 remaining 5 added, 5 withdrawn, 0 remaining [4,4] added

问题 I'm doing a Simulated Annealing algorithm to optimise a given allocation of students and projects. This is language-agnostic pseudocode from Wikipedia: s ← s0; e ← E(s) // Initial state, energy. sbest ← s; ebest ← e // Initial "best" solution k ← 0 // Energy evaluation count. while k < kmax and e > emax // While time left & not good enough: snew ← neighbour(s) // Pick some neighbour. enew ← E(snew) // Compute its energy. if enew < ebest then // Is this a new best? sbest ← snew; ebest ← enew /

问题 I want to solve the following optimization problem with Python: I have a black box function f with multiple variables as input. The execution of the black box function is quite time consuming, therefore I would like to avoid a brute force approach. I would like to find the optimum input parameters for that black box function f . In the following, for simplicity I just write the dependency for one dimension x . An optimum parameter x is defined as: the cost function cost(x) is maximized with