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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Pareto originally used the word "optimal" for the concept, but this is somewhat of a misnomer: Pareto's concept more closely aligns with an idea of "efficiency", because it does not identify a single "best" (optimal) outcome.
In multi-objective optimization, the Pareto front (also called Pareto frontier or Pareto curve) is the set of all Pareto efficient solutions. [1] The concept is widely used in engineering . [ 2 ] : 111–148 It allows the designer to restrict attention to the set of efficient choices, and to make tradeoffs within this set, rather than ...
The choice among "Pareto optimal" solutions to determine the "favorite solution" is delegated to the decision maker. In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other.
The first fundamental welfare theorem provides some basis for the belief in efficiency of market economies, as it states that any perfectly competitive market equilibrium is Pareto efficient. The assumption of perfect competition means that this result is only valid in the absence of market imperfections, which are significant in real markets.
Since all Pareto-dominating allocations are not feasible, (,) must itself be Pareto optimal. [ 38 ] Note that while the fact that Y ∗ {\displaystyle \mathbf {Y^{*}} } is profit maximizing is simply assumed in the statement of the theorem the result is only useful/interesting to the extent such a profit maximizing allocation of production is ...
An allocation of goods is said to 'Pareto dominate' another if it is preferable for one consumer and no worse for the other. An allocation is said to be 'Pareto optimal' (or 'Pareto efficient') if no other allocation Pareto dominates it. The set of Pareto optimal allocations is known as the Pareto set (or 'efficient locus').
Every Pareto efficient social choice function is necessarily a utilitarian choice function, a result known as Harsanyi's utilitarian theorem. Specifically, any Pareto efficient social choice function must be a linear combination of the utility functions of each individual utility function (with strictly positive weights).