I have the following fitness function:
function f = objfun(x,t)
f = x.*(t-x);
end
When i try to use this code as a fitness function using MATLAB's Optimization Tool and the Genetic Algorithm (ga) solver, i get the following error:
Error running optimization. Not enough input arguments.
I know the function has only 2 variables and I'm passing it those few variables so I have no idea why I am getting this error.
Can someone please help me fix this?
I never worked in Matlab because I heard that is slow (see for instance this thread: Performance Tradeoff - When is MATLAB better/slower than C/C++).
for genetic algorithms you need the highest speed possible because for complex problems you need very large populations...
I suggest to use C/C++. Here is a very light C implementation of a Genetic Algorithm that I've made for solving the function optimization problem: http://create-technology.blogspot.ro/2015/03/a-genetic-algorithm-for-solving.html
Related
I am using CPLEX for MATLAB toolbox wherein I formulate my MILP as a huge matrix and use the function cplexmilp to call the solver. Since the model I am solving is really huge, I intend to set the option of using multiple processors to speed up the solving of the MILP. I went through the manual but I could not find any specific solution for my case. I'd be grateful if anyone could help me find a solution to this issue.
CPLEX Toolbox Function
Thanks Everyone
I have a program using PSO algorithm using penalty function for Constraint Satisfaction. But when I run the program for different iterations, the output of the algorithm would be :
"Iteration 1: Best Cost = Inf"
.
Does anyone know why I always get inf answer?
There could be many reasons for that, none of which will be accurate if you don't provide a MWE with the code you have already tried or a context of the function you are analysing.
For instance, while studying the PSO algorithm you might use it on functions which have analytical solutions first. By doing this you can study the behaviour of the algorithm before applying to a similar problem, and fine tune its parameters.
My guess is that you might not be providing either the right function (I have done that already, getting a signal wrong is easy!), the right constraints (same logic applies), your weights for the penalty function and velocity update are way off.
I have a function fun(x,y,z), such that say, x=1:10, y=50:60, z=100:105. Which optimization method (and how) I can use to get the minimum of this function, for example, where (x,y,z)=(3,52,101). I am working in Matlab.
Thank you for any help
Algorithms
There are many many algorithms out there that you can use for direct search optimization such as Nelder-Mead, Particle Swarm, Genetic Algorithm, etc.
I believe Nelder-Mead is a simplex optimization method which is used by fminsearch function in MATLAB.
Also, there is Genetic Algorithm which comes with MATLAB Global Optimization toolbox. You may want to give that a try as well.
Particle Swarm Optimization (PSO) is another direct search method that you can use. However, there is no official toolbox for Particle Swarm method built by Mathworks. The good news is there is quite a few PSO toolbox developed by other people. I personally have used this one and am quite happy with the performance. Its syntax is similar to Genetic Algorithm functions that come with Global Optimization Toolbox.
Discrete Optimization
Regarding your question that you are looking for a set of integer values namely x,y, and z corresponding to the minimum objective function value, I would add a part at the beginning of the objective function that rounds the variables to the closest integers and then feeds them to your main function fun(x,y,z). This way you would discretize your function space.
I hope my answer helps.
I'm using the genetic algorithm from the MATLAB Global Optimization Toolbox with SimEvents, in order to implement a mixed integer optimization making use of simulation outputs to evaluate the fitness function. My model is pretty similar to the one described in this video from MathWorks website:
http://www.mathworks.it/videos/optimizing-manufacturing-production-processes-68961.html
Reading the documentation, I found that ga can solve constrained problems only if such constraints are linear inequalities. The constraints are supposed to be written as functions of the problem's variables, that in this case are the number of resources used during the simulation.
I would like, instead, to set a constraint that takes into account another simulation output (e.g. the drain utilization), i.e. minimize
objfun = backlog*10000 + cost
where backlog is a simulation output (obtained using simOut.get), considering the following constraint:
drain_utilization > 0.7
where drain_ utilization is another simulation output (again, obtained using simOut.get).
Is it possible or this feature is not supported by the Global Optimization Toolbox?
Thank you in advance and forgive me for any improper term, but I'm new to the Global Optimization Toolbox.
I have been trying to model the Fabry-Perot resonator in simulink. I am not sure if it is right to choose simulink for this task but I have been getting some results, at least. However, I have been also getting an error of algebraic loop when I use a different pair of coupling/reflection parameters. It says,
"Simulink cannot solve the algebraic loop containing
'jblock_multi_MR/Meander2b/Subsystem3/Real-Imag to Complex' at time
6.91999999999991 using the LineSearch-based algorithm due to one of the
following reasons: the model is ill-defined i.e., the system equations do
not have a solution; or the nonlinear equation solver failed to converge
due to numerical issues.
To rule out solver convergence as the cause of this error, either
a) switch to TrustRegion-based algorithm using
set_param('jblock_multi_MR','AlgebraicLoopSolver','TrustRegion')
b) reducing the VariableStepDiscrete solver RelTol parameter so that
the solver takes smaller time steps.
If the error persists in spite of the above changes, then the model is
likely ill-defined and requires modification."
Changing the solver does not help. As a note, I implemented the system in terms of electric fields and complex signals naturally.
thanks for any help.
There is no magic solution for solving algebraic loop issues, as these issues tend to be very model-dependent. Here are a few pointers though:
What are algebraic loops in Simulink and how do I solve them?
How can I resolve algebraic loops in my Simulink model in Simulink 6.5 (R2006b)?
Algebraic Loops in the Simulink documentation
See also this answer to a similar question on SO, with some suggestions for breaking the loop.