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Let's say there is a single objective optimization problem SP (e.g. a linear program) and an optimization algorithm SA for it (e.g. simplex). Then applying SA to SP yields an optimal solution s=SA(SP).

Now let's assume there is a multi objective optimization problem MP (e.g. a linear program with two linear objectives) but there is no access to a multi objective optimization algorithm to compute the (Pareto-optimal) solutions (front).

One option is of course to linearise the MP to turn it into a SP, basically the result is a weighted sum (in the example of two terms) but this obviously does not result in all Pareto-optimal solutions but it totally depends on the chosen weights.

Is there any way to iteratively apply the single objective algorithm SA to the multi objective MP to mimic a multi objective optimization algorithm MA?

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Actually, using a weighted sum does find all Pareto-optimal solutions.

Let your two objective functions be $f_1,f_2$. Define a single objective function

$$f(x) = \alpha f_1(x) + (1-\alpha) f_2(x),$$

where $\alpha$ is a constant. If you sweep $\alpha$ over all possible values in $[0,1]$ and, for each such $\alpha$, solve the single-objective optimization problem (minimizing $f$), then the set of solutions you obtain includes all Pareto-optimal solutions to the original multi-objective optimization problem.

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