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### Highlights from GODLIKE - A robust single-& multi-objective optimizer

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# GODLIKE - A robust single-& multi-objective optimizer

24 Jul 2009 (Updated )

GODLIKE combines 4 global optimizers for both single/multi-objective optimizations

File Information
Description

GODLIKE (Global Optimum Determination by Linking and Interchanging Kindred Evaluators) is a generization of various population-based global optimization schemes. Also, it handles both single- and multi-objective optimization, simply by adding additional objective functions.

GODLIKE solves optimization problems using relatively basic implementations of a genetic algorithm, differential evolution, particle swarm optimization and adaptive simulated annealing algorithms. Its power comes from the fact that these different algorithms run simultaneously (linked), and members from each population are occasionally swapped (interchanged) to decrease the chances of convergence to a local minimizer.

It is primarily intended to increase ROBUSTNESS, not efficiency as it usually requires more function evaluations than any of the algorithms separately. Its also inteded to do away with the need to fine-tune these algorithms each and every time you encounter an optimization problem, AND to generalize optimization itself (it's both a single and multi-objective optimizer), AND to generate simple plots to be used in quick reports etc.

BASIC EXAMPLES:

(single-objective)

% extended rosenbruck function
rosen = @(X) sum( 100*(X(:, 2:2:end) - X(:, 1:2:end-1).^2).^2 + (1 - X(:, 1:2:end-1)).^2, 2);

% call GODLIKE
GODLIKE(rosen, 250, -10*ones(1,10), 10*ones(1,10), 'ms')

will produce a reasonably accurate approximation to the global minimum of the 10-dimensional rosenbruck problem
( sol ~ ([1,1,1,...]), fval ~ 0 )

(multi-objective optimization)

% basic Sin-Cos Pareto front
GODLIKE({@sin;@cos}, 100, 0, 2*pi, [], 'display', 'plot')

will generate a nice plot of the problem's Pareto front. Some more examples are included in the GODLIKE_DEMO.m, included in the ZIP.

======================================
MAJOR CHANGES
(see the changelog for more detailed changes)
======================================
(06/Aug/2009)
- Objective functions can now accept any 2-dimensional input. Your objective function should accept arguments equal in size to either [lb] or [ub], and return a simple scalar.
- I discovered I made some *severe* mistakes in the implementation of the global optimization algorithms. This caused large inefficiencies or inaccurate results. Most (hopefully all) of these mistakes are corrected now.
- Added 2more options for the algorithms: NetWorkTopology & ReHeat (see doc)
- Changed the [MinDescent] criterion to the more MATLAB-style 'TolX' and 'TolFun' options

Acknowledgements
MATLAB release MATLAB 7.7 (R2008b)
28 Oct 2013

nice job !

18 Apr 2013

Dear Roddy

Thank you for sharing your great "godlike" optimisation code in matlab
central. I have two questions:
1. With the current "godlike" demo (set by you) the Multi-objective
optimisation is implemented. How can I set it to run just
single-objective optimisation?
2. How can I set it to run just one heuristic technique?

08 Mar 2012
18 Nov 2011

Hello Rody, does this tool solve discrete optimization problems...do you have any update for solving discrete problems

26 Aug 2011
23 Jun 2011
22 Jun 2011

I've found that this code is quite helpful in generating useful initial conditions for some constrained optimization problems that involve local minima. Are there any plans to handle nonlinear inequality constraints? Also, a useful addition to the code would be an option for a maximum run time. That way I can set it to run over the weekend easily.

28 Jan 2011

When I try the multi-objective minimization I face some issues.
Let's take as an example this multiobjective function
function a = myfunc(x)
a(1) = sin(x)
a(2) = cos(x)

if I use GODLIKE like this:
GODLIKE({@sin,@cos},...)
it works fine

in this way
optfunc = @(x) myfunc(x)
GODLIKE(optfunc,...)
id does not.

What am I doing wrong in the second case? How should I use it?

07 Dec 2010

How to pass parameters to the objective function?

27 Sep 2010

Thanks Rody...But I need the codes for integer programming. How can use these codes for that purpose.. i.e. which part of code, must be changed to achieve my purpose?

03 Sep 2010

Developed function is very nice, but can i handle equality and inequality constraints in GODLIKE? In user manual, i couldn't find anything related to this.

25 Aug 2010

Is it possible to add more constraints into the optimization of godlike? for example: x(1)+x(2)-3*x(3)=10
How could I realize that in matlab?
Thanks

10 Jul 2010

trade extra cpu cycles for fewer brain cycles.

15 Oct 2009

I would like to implement this code to fit a non-linear equation to a set of data. The non-linear equation takes 8 paramaters which I would like to optomize. Basically, the sum of least squares approach. Will GODLIKE achieve this? I have tried using it and the paramater guesses are all over the place and do not seem to converge. Thank you!

29 Sep 2009

A bugfix.
set_options.m, at lines 456-457:

elseif strcmpi(option, 'SkipTest')
if ~isnumeric(value)

should be:

elseif strcmpi(option, 'SkipTest')
if ~ischar(value)

08 Sep 2009

Daniel: I am most certainly planning to implement (non)-linear constraints in GODLIKE, that would be pretty convenient indeed! However, I'm VERY busy the next few weeks, so don't expect this change to come anytime soon :)

You could try my other tool OPTIMIZE (also on the file-exchange); it can also optimize problems globally. Perhaps that can give you some results...

Thanks for the 5 stars ! :)

08 Sep 2009

Nice tool! Any plans for adding non-linear constraint handling? I'm messing with a Lagrangian barrier function (the method the original C++ implementation of NSGAII uses) inside a custom objective function, but it would be nice to have that kind of functionality implemented directly in the optimizer.

02 Sep 2009

Roland: Indeed, I overlooked that issue. It works great, thanks!

About the mex-file: I see no real need for it. Most of the time the real computational cost is in the objective functions, not this optimizer (as it should be)...except maybe for the NSGA-II part, but really I only notice that it's N2-complex if I use huge population sizes...that, and I'm really short on time these days :) But of course, you are more than welcome to do it!

So what changes did you make to allow ML2007a to run it? It would be perfect if more users could use GODLIKE! I think it's a great idea if you incorporate those changes, write a mex file and submit it as an improvement upon my version.

Thanks for the feedback.

02 Sep 2009

Hi, You can make it orders of magnitude faster if You replace in pop_single.m this:

for i = 1:pop.dimensions
% convert column to decimal representation
temp_pop(:, i) = sum(convert_to_dec.*newpop(:, 1:NumBits), 2);
% delete entries
newpop(:, 1:NumBits) = [];
end

with this:

newpop_startcol = 1;
NumBits2 = NumBits-1;
for i = 1:pop.dimensions
% convert column to decimal representation
temp_pop(:, i) = sum(convert_to_dec.*newpop(:, newpop_startcol : newpop_startcol+NumBits2), 2);
% delete entries
newpop_startcol = newpop_startcol + NumBits;
end

Column/row deletion is very slow operation, its better to avoid it.

There were also some changes I had to make in order for the script to start in Matlab R2007a.

Are You considering to port it to mex file? I am going to do that but we may get the results sooner if we split the task.

03 Aug 2009