I am planning to use ant colony optimization (ACO) to optimize the layout design of a building. I want to use Anylogic to derive the performance of a certain layout design in each iteration of the ACO algorithm and finally obtain the best solution.
Is it necessary to use the APIs of anylogic to realize this idea ?
If the answer is Yes, then , it is possible to change layout design in Anylogic in each iteration of ACO automatically through API?
To use a customized optimization algorithm, the optimization experiment obviously doesn't work because it uses its own heuristics.
You can do that only with a custom experiment. In this custom experiment you will generate the ACO and run the simulation as many times as needed and collect the results to feed the next iteration.
I have done this with Artificial bee colony, cuckoo search and genetic algorithms to optimize systems, so it should be the same for you.
Nevertheless since you are trying to optimize a layout, you will have to smart on how you define your objective function restrictions and search parameters... but that's off-topic
Related
I try to apply One Class SVM but my dataset contains too many features and I believe feature selection would improve my metrics. Are there any methods for feature selection that do not need the label of the class?
If yes and you are aware of an existing implementation please let me know
You'd probably get better answers asking this on Cross Validated instead of Stack Exchange, although since you ask for implementations I will answer your question.
Unsupervised methods exist that allow you to eliminate features without looking at the target variable. This is called unsupervised data (dimensionality) reduction. They work by looking for features that convey similar information and then either eliminate some of those features or reduce them to fewer features whilst retaining as much information as possible.
Some examples of data reduction techniques include PCA, redundancy analysis, variable clustering, and random projections, amongst others.
You don't mention which program you're working in but I am going to presume it's Python. sklearn has implementations for PCA and SparseRandomProjection. I know there is a module designed for variable clustering in Python but I have not used it and don't know how convenient it is. I don't know if there's an unsupervised implementation of redundancy analysis in Python but you could consider making your own. Depending on what you decide to do it might not be too tricky (especially if you just do correlation based).
In case you're working in R, finding versions of data reduction using PCA will be no problem. For variable clustering and redundancy analysis, great packages like Hmisc and ClustOfVar exist.
You can also read about other unsupervised data reduction techniques; you might find other methods more suitable.
we want to publish an Open-Source for integrating Reinforcement Learning to Smartgrid optimization.
We use OpenModelica as GUI, PyFMI for the import to Python and Gym.
Nearly everything is running, but a possibility to connect or disconnect additional loads during the simulation is missing. Everything we can do for now is a variation of the parameters of existing loads, which gives some flexibility, but way less then the possibility to switch loads on and off.
Using the implemented switches in OpenModelica is not really an option. They just place a resistor at this spot, giving it either a very low or very high resistance. First, its not really decoupled, and second, high resistances make the ODE-system stiff, which makes it really hard (and costly) to solve it. In tests our LSODA solver (in stiff cases basically a BDF) ran often in numerical errors, regardless of how the jacobian was calculated (analytically by directional derivatives or with finite differences).
Has anyone an idea how we can implement a real "switching effect"?
Best regards,
Henrik
Ideal connection and disconnection of components during simulation
requires structure variability, which is not fully supported
by Modelica (yet). See also this answer https://stackoverflow.com/a/30487641/8725275
One solution for this problem is to translate all possible
model structures in advance and switch the simulation model if certain conditions are met. As there is some overhead involved, this approach only makes sense, when the model is not switched very often.
There is a python framework, which was built to support this process: DySMo. The tool was written by Alexandra Mehlhase, who made a lot of interesting publications regarding structure variability, e.g. An example of beneficial use of
variable-structure modeling to enhance an existing rocket model.
The paper Simulating a Variable-structure Model of an Electric Vehicle for Battery Life Estimation Using Modelica/Dymola and Python of Moritz Stueber is also worth a look. It contains a nice introduction about variable structure systems and available solutions.
I am currently running a multiple linear regression using MATLAB's LinearModel.fit function, and I am bit confused in regards to how to properly add interaction terms to the model by hand. As I am aware, LinearModel.fit does not standardize variables on its own, so I have been doing so manually.
So far, the way I have done it has been to
Standardize the observations for each variables
Multiply corresponding standardized values from specific variables to create the interaction terms and then add these new variables to the set of regression data
Run the regression
Is this the correct way to go about doing this? Should I standardize the interaction term variables also after calculating the 'raw' terms? Any help would be greatly appreciated!
Whether or not to standardize interaction terms probably depends on what you intend to do with the model. Standardization typically does not affect model performance as much as it allows for more straightforward model interpretation as your learned coefficients will be on similar scales. I suspect whether to do this or not is largely a matter of opinion. Here is a relevant stats.stackexchange post that may help.
My intuition would be the same as how you have described your process so far.
I am currently developing a large and complex thermo-hydraulic systems in Modelica/Dymola environment using ThermoPower library by Prof. Francesco Casella. At present, I have completed building our system model (which contains several closed-loop hydraulic circuits) and concentrating on designing controllers for the developed model. Given complexity of the system, I have about 25 PI controllers controlling various valve opening, pump, condenser and boilers. At this stage, I am tuning the controller gains using some judicious trial-and-error method. I tried to look into literature to see if there are any formal design methodology or any rule-of-thumbs for designing controllers for such a multi-input-multi-output (MIMO) thermo-hydraulic system. Consequently, I would like to ask if anyone can provide some pointers or literature/papers which deals with controller designs for such systems. Because my knowledge in controller design (sliding mode, linear control, root locus, etc) are not helping me here as most of these methodology are based on available model equations.
Furthermore, for such a large thermo-hydraulic systems, how one sets initial conditions of the system? Does one need to just provide some reasonable guess value and expect Dymola to take care of rest of it?
Well, I have to qualify my response by pointing out that I am NOT a controls engineer so take everything I say with a grain of salt.
To some extent, it really depends on what tool you are using since different tools specialize in different analysis features and offer different capabilities. For example, if you are using Dymola, you can use the "linearize" function to linearize your system. This will give you an entry into the formal controller design methods you are familiar with. The problem is, of course, that your system is probably highly non-linear so you will have to formulate a strategy to determine over what range of state space you need to control and then potentially develop strategies to adjust your gains accordingly.
One the other hand, if you are using tools like SystemModeler (from Wolfram) or MapleSim (from Maplesoft), I'm pretty sure you have the option to elaborate the Modelica model into a symbolic system of equations. As a result, you can again revisit the classical techniques that require the model equations to be available. Since these are not linearized, you will have full visibility on the non-linearities in symbolic form and you can take whatever measures are possible to address them.
Does that help?
I would try Model Predictive Control in your case (as long as your system will only be active in an approximately linear region or it can be made approximately linear).
Here is some info:
http://www.stanford.edu/class/archive/ee/ee392m/ee392m.1056/Lecture14_MPC.pdf
But I would recommend getting a good control engineer book that describes this in more detail.
It has been quite a few years back that I have done an example of this so maybe this suggestion is outdated now.
Note that when you implement this in Modelica/Dymola that you will have to simulate the model using a fixed time step solver.
I have to solve a multiobjective problem but I don't know if I should use CPLEX or Matlab. Can you explain the advantage and disadvantage of both tools.
Thank you very much!
This is really a question about choosing the most suitable modeling approach in the presence of multiple objectives, rather than deciding between CPLEX or MATLAB.
Multi-criteria Decision making is a whole sub-field in itself. Take a look at: http://en.wikipedia.org/wiki/Multi-objective_optimization.
Once you have decided on the approach and formulated your problem (either by collapsing your multiple objectives into a weighted one, or as series of linear programs) either tool will do the job for you.
Since you are familiar with MATLAB, you can start by using it to solve a series of linear programs (a goal programming approach). This page by Mathworks has a few examples with step-by-step details: http://www.mathworks.com/discovery/multiobjective-optimization.html to get you started.
Probably this question is not a matter of your current concern. However my answer is rather universal, so let me post it here.
If solving a multiobjective problem means deriving a specific Pareto optimal solution, then you need to solve a single-objective problem obtained by scalarizing (aggregating) the objectives. The type of scalarization and values of its parameters (if any) depend on decision maker's preferences, e.g. how he/she/you want(s) to prioritize different objectives when they conflict with each other. Weighted sum, achievement scalarization (a.k.a. weighted Chebyshev), and lexicographic optimization are the most widespread types. They have different advantages and disadvantages, so there is no universal recommendation here.
CPLEX is preferred in the case, where (A) your scalarized problem belongs to the class solved by CPLEX (obviously), e.g. it is a [mixed integer] linear/quadratic problem, and (B) the problem is complex enough for computational time to be essential. CPLEX is specialized in the narrow class of problems, and should be much faster than Matlab in complex cases.
You do not have to limit the choice of multiobjective methods to the ones offered by Matlab/CPLEX or other solvers (which are usually narrow). It is easy to formulate a scalarized problem by yourself, and then run appropriate single-objective optimization (source: it is one of my main research fields, see e.g. implementation for the class of knapsack problems). The issue boils down to finding a suitable single-objective solver.
If you want to obtain some general information about the whole Pareto optimal set, I recommend to start with deriving the nadir and the ideal objective vectors.
If you want to derive a representation of the Pareto optimal set, besides the mentioned population based-heuristics such as GAs, there are exact methods developed for specific classes of problems. Examples: a library implemented in Julia, a recently published method.
All concepts mentioned here are described in the comprehensive book by Miettinen (1999).
Can cplex solve a pareto type multiobjective one? All i know is that it can solve a simple goal programming by defining the lexicographical objs, or it uses the weighted sum to change weights gradually with sensitivity information and "enumerate" the pareto front, which highly depends on the weights and looks very subjective.
You can refer here as how cplex solves the bi-objetive one, which seems not good.
For a true pareto way which includes the ranking, i only know some GA variants can do like NSGA-II.
A different approach would be to use a domain-specific modeling language for mathematical optimization like YALMIP (or JUMP.jl if you like to give Julia a try). There you can write your optimization problem with Matlab with some extra YALMIP functionalities and use CPLEX (or any other supported solver as a backend) without restricting to one solver.