Multi-objective environments

When solving a multi-objective model, the solution process typically proceeds in phases, where each phase solves for one objective. The standard algorithmic parameters influence the strategy used to solve the overall multi-objective model. However, in some cases you may want finer-grain control over the strategies used in each phase. The solver enables this through multi-objective environments.

Multi-objective environments are created via API routines (in C, C++, Java, .NET, or Python). You set parameters on these environments as you would with any other environment, but in this case they only affect one of the several objective solves.

To give a simple example, in Python you could do the following:

  # Create multi-objective environments
  env0 = model.getMultiobjEnv(0)
  env1 = model.getMultiobjEnv(1)

  # Set parameters on multi-objective environments
  env0.setParam('Method', 2)
  env1.setParam('Method', 1)
  env1.setParam('Presolve', 0)

  # Perform multi-objective optimization
This would use the barrier solver (Method=2) for the first objective, and the dual simplex solver (Method=1) with no presolve (Presolve=0) for the second. Note that you don't need a multi-objective environment for each objective - only for those where you want parameters to take different values from those of the model itself.

Please note that parameter values are copied from the model when the multi-objective environment is created. Therefore, changes to parameter values on the model have no effect on multi-objective environments that have already been created. This is a frequent source of confusion.

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