Pareto optimality refers to an allocation of resources where it is not possible to make one person better off without making another person worse off. A perfectly competitive market can achieve a Pareto optimal allocation of resources. In game theory, a Pareto optimal outcome is one where no player can be better off without making another player worse off. Multi-objective optimization involves simultaneously optimizing two or more conflicting objectives subject to constraints. Evolutionary algorithms are commonly used to approximate the entire Pareto front of optimal solutions in a single run.

1. Pareto optimal
A Pareto optimal outcome is one such that no-one could be made better off without making
someone else worse off.
The concept of Pareto optimality occurs in a number of areas of economics. The allocation of
resources in an economy is Pareto optimal, often called Pareto efficient, if it is not possible to
change the allocation of resources in such a way as to make some people better off without
making others worse off.
A perfectly competitive market can be shown to deliver a Pareto optimal allocation of
resources. Whether this is the most desirable allocation of resources is matter of a value
judgement.
In game theory a Pareto optimal outcome is one in which no player could be better off without
another becoming worse off. A Nash equilibrium, and other outcomes that can be predicted,
may not be Pareto optimal.
Multi-objective Optimization:
MOP is the process of simultaneously optimizing two or more conflicting objectives
subject to certain constraints.
Multi-objective optimization problems can be found in various fields: product and process
design, finance, aircraft design, the oil and gas industry, automobile design, or wherever
optimal decisions need to be taken in the presence of trade-offs between two or more conflicting
objectives. Maximizing profit and minimizing the cost of a product; maximizing performance
and minimizing fuel consumption of a vehicle; and minimizing weight while maximizing the
strength of a particular component are examples of multi-objective optimization problems.
For nontrivial multi-objective problems, one cannot identify a single solution that
simultaneously optimizes each objective. While searching for solutions, one reaches points such
that, when attempting to improve an objective further, other objectives suffer as a result. A
tentative solution is called non-dominated, Pareto optimal, or Pareto efficient if it cannot be
eliminated from consideration by replacing it with another solution which improves an
objective without worsening another one. Finding such non-dominated solutions, and
quantifying the trade-offs in satisfying the different objectives, is the goal when setting up and
solving a multiobjective optimization problem.
Evolutionary algorithms
Evolutionary algorithms are popular approaches to solving multiobjective optimization.
Currently most evolutionary optimizers apply Pareto-based ranking schemes. Genetic

2. algorithms such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength
Pareto Evolutionary Algorithm 2 (SPEA-2) have become standard approaches, although some
schemes based on particle swarm optimization and simulated annealing[12] are significant.
The main advantage of evolutionary algorithms, when applied to solve multi-objective
optimization problems, is the fact that they typically optimize sets of solutions, allowing
computation of an approximation of the entire Pareto front in a single algorithm run. The main
disadvantage of evolutionary algorithms is the much lower speed.
Other methods
Multiobjective Optimization using Evolutionary Algorithms (MOEA).
PGEN (Pareto surface generation for convex multiobjective instances)
IOSO (Indirect Optimization on the basis of Self-Organization)
SMS-EMOA (S-metric selection evolutionary multiobjective algorithm)
Reactive Search Optimization (using machine learning for adapting strategies and
objectives) implemented in LIONsolver
Benson's algorithm for linear vector optimization problems
Applications
Economics
In economics, the study of resource allocation under scarcity, many problems involve multiple
objectives along with constraints on what combinations of those objectives are attainable.
For example, a consumer's demands for various goods are determined by the process of
maximization of the utility derived from those goods, subject to a constraint based on how
much income is available to spend on those goods and on the prices of those goods. This
constraint allows more of one good to be purchased only at the sacrifice of consuming less of
another good; therefore, the various objectives (more consumption of each good is preferred)
are in conflict with each other according to this constraint. A common method for analyzing
such a problem is to use a graph of indifference curves, representing preferences, and a budget
constraint, representing the trade-offs that the consumer is faced with.
Another example involves the production possibilities frontier, which specifies what
combinations of various types of goods can be produced by a society with certain amounts of
various resources. The frontier specifies the trade-offs that the society is faced with — if the
society is fully utilizing its resources, more of one good can be produced only at the expense of
producing less of another good. A society must then use some process to choose among the
possibilities on the frontier.

3. Macroeconomic policy-making is a context requiring multi-objective optimization. Typically a
central bank must choose a stance for monetary policy that balances competing objectives —
low inflation, low unemployment, low balance of trade deficit, etc. To do this, the central bank
uses a model of the economy that quantitatively describes the various causal linkages in the
economy; it simulates the model repeatedly under various possible stances of monetary policy,
in order to obtain a menu of possible predicted outcomes for the various variables of interest.
Then in principle it can use an aggregate objective function to rate the alternative sets of
predicted outcomes, although in practice central banks use a non-quantitative, judgement-
based, process for ranking the alternatives and making the policy choice.
Finance
In finance, a common problem is to choose a portfolio when there are two conflicting objectives
— the desire to have the expected value of portfolio returns be as high as possible, and the
desire to have risk, measured by the standard deviation of portfolio returns, be as low as
possible. This problem is often represented by a graph in which the efficient frontier shows the
best combinations of risk and expected return that are available, and in which indifference
curves show the investor's preferences for various risk-expected return combinations. The
problem of optimizing a function of the expected value (first moment) and the standard
deviation (square root of the second moment) of portfolio return is called a two-moment
decision model.
Linear programming applications
In linear programming problems, a linear objective function is optimized subject to linear
constraints. Typically multiple variables of concern appear in the objective function. A vast
body of research has been devoted to methods of solving these problems. Because the efficient
set, the set of combinations of values of the various variables of interest having the feature that
none of the variables can be given a better value without hurting the value of another variable,
is piecewise linear and not continuously differentiable, the problem is not dealt with by first
specifying all the points on the Pareto-efficient set; instead, solution procedures utilize the
aggregate objective function right from the start.
Many practical problems in operations research can be expressed as linear programming
problems. Certain special cases of linear programming, such as network flow problems and
multi-commodity flow problems are considered important enough to have generated much
research on specialized algorithms for their solution. Linear programming is heavily used in
microeconomics and company management, for dealing with such issues as planning,
production, transportation, technology, and so forth.

4. Optimal control applications
Main articles: Optimal control, Dynamic programming, and Linear-quadratic regulator
In engineering and economics, many problems involve multiple objectives which are not
describable as the-more-the-better or the-less-the-better; instead, there is an ideal target value
for each objective, and the desire is to get as close as possible to the desired value of each
objective. For example, one might want to adjust a rocket's fuel usage and orientation so that it
arrives both at a specified place and at a specified time; or one might want to conduct open
market operations so that both the inflation rate and the unemployment rate are as close as
possible to their desired values.
Often such problems are subject to linear equality constraints that prevent all objectives from
being simultaneously perfectly met, especially when the number of controllable variables is less
than the number of objectives and when the presence of random shocks generates uncertainty.
Commonly a multi-objective quadratic objective function is used, with the cost associated with
an objective rising quadratically with the distance of the objective from its ideal value. Since
these problems typically involve adjusting the controlled variables at various points in time
and/or evaluating the objectives at various points in time, intertemporal optimization
techniques are employed.