optimisation mathématique - peut l'algorithme génétique dans Matlab passez une deuxième valeur de retour de la remise en forme-fonction aux contraintes ? -Débordement de pile

I am simulating a batch evaporator in Matlab. The genetic algorithm varies several starting variables x (such as size, max. mass of working fluid, the overall number of evaporators used ...) and the goal is to maximize the efficiency. So my function evaporator(x) returns the negative efficiency which is minimized by the algorithm.
Besides the efficiency, there are several other values calculated. One is the duration of the simulated cycle (NOT the runtime of the calculation itself!). As a constraint, the duration of the whole evaporation cycle should, depending on the number of evaporators, not be to short (e.g. if three evaporators are used, the whole cycle should take at least 3*5s = 15s).
I know that I can easily use the nonlinear constraints option of ga (nonlcon). In this case I would just have to do the same calculations from evaporator(x) again, but return the calculated duration this time. The problem is, that I have to call an external DLL several thousand times per run and the calculations become really slow. Therefore I really want to avoid running all the calculations twice.
Is it somehow possible, that evaporator(x) returns both the negative efficiency and the duration at the same time? ga should then minimize the negative efficiency and evaluate the duration regarding the constraints. I thought about nesting the evaporator(x) inside an anonymous function, but I think my function still has to be called twice then?

I had good experience with the Multiobjective Optimization of the efficiency and the duration at the same time, but unfortunately the gamultiobj algorithm cannot handle integer variables.

At the moment I am using a penalty function on short durations inside evaporator(x), but I think, that the constraints handling of the ga algorithm would be better than this.

One option is to include some persistent state, which records both the efficiency and duration computed on the last run (this is memoization) and decide which to return based on the function inputs.

For example,

function return_value = evaporator(method, value, inputs)

  persistent efficiency duration;

  if strcmp(method, 'recalculate')
      % Some lengthy calculation goes here
      efficiency = inputs + 1;
      duration   = inputs + 10;
      pause(5);
  end

  switch value
    case 'efficiency'
      return_value = efficiency;
    case 'duration'
      return_value = duration;
  end

end

The result is

>> evaporator('recalculate', 'efficiency', 10)
11
>> evaporator('memoized', 'duration')
20
>> evaporator('recalculate', 'efficiency', 20)
21
>> evaporator('memoized', 'duration')
30

I am simulating a batch evaporator in Matlab. The genetic algorithm varies several starting variables x (such as size, max. mass of working fluid, the overall number of evaporators used ...) and the goal is to maximize the efficiency. So my function evaporator(x) returns the negative efficiency which is minimized by the algorithm.
Besides the efficiency, there are several other values calculated. One is the duration of the simulated cycle (NOT the runtime of the calculation itself!). As a constraint, the duration of the whole evaporation cycle should, depending on the number of evaporators, not be to short (e.g. if three evaporators are used, the whole cycle should take at least 3*5s = 15s).
I know that I can easily use the nonlinear constraints option of ga (nonlcon). In this case I would just have to do the same calculations from evaporator(x) again, but return the calculated duration this time. The problem is, that I have to call an external DLL several thousand times per run and the calculations become really slow. Therefore I really want to avoid running all the calculations twice.
Is it somehow possible, that evaporator(x) returns both the negative efficiency and the duration at the same time? ga should then minimize the negative efficiency and evaluate the duration regarding the constraints. I thought about nesting the evaporator(x) inside an anonymous function, but I think my function still has to be called twice then?

I had good experience with the Multiobjective Optimization of the efficiency and the duration at the same time, but unfortunately the gamultiobj algorithm cannot handle integer variables.

At the moment I am using a penalty function on short durations inside evaporator(x), but I think, that the constraints handling of the ga algorithm would be better than this.

One option is to include some persistent state, which records both the efficiency and duration computed on the last run (this is memoization) and decide which to return based on the function inputs.

For example,

function return_value = evaporator(method, value, inputs)

  persistent efficiency duration;

  if strcmp(method, 'recalculate')
      % Some lengthy calculation goes here
      efficiency = inputs + 1;
      duration   = inputs + 10;
      pause(5);
  end

  switch value
    case 'efficiency'
      return_value = efficiency;
    case 'duration'
      return_value = duration;
  end

end

The result is

>> evaporator('recalculate', 'efficiency', 10)
11
>> evaporator('memoized', 'duration')
20
>> evaporator('recalculate', 'efficiency', 20)
21
>> evaporator('memoized', 'duration')
30

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