Multiobjective Optimization

Abstract

Multiobjective optimization caters to achieving multiple goals, subject to a set of constraints, with a likelihood that the objectives will conflict with each other. Multiobjective optimization can also be explained as a multicriteria decision-making process, in which multiple objective functions have to be optimized simultaneously. In many cases, optimal decisions may require tradeoffs between conflicting objectives. Traditional optimization schemes use a weight vector to specify the relative importance of each objective and then combine the objectives into a scalar cost function. This strategy reduces the complexity of solving a multiobjective problem by converting it into a single-objective problem. Solution techniques for multiobjective optimization involve a tradeoff between model complexity and accuracy. Examples of multiobjective optimization can be found in economics (setting monetary policy), finance (risk–return analysis), engineering (process control, design tradeoff analysis), and many other applications in which conflicting objectives must be obtained.