11 Facts About Multi-objective optimization

Multi-objective 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.

Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Multi-objective optimization problem is an optimization problem that involves multiple objective functions.

Pareto front of a multi-objective optimization problem is bounded by a so-called nadir objective vector and an ideal objective vector, if these are finite.

Multi-objective design optimization have been implemented in engineering systems in circumstances such as control cabinet layout optimization, airfoil shape optimization using scientific workflows, design of nano-CMOS semiconductors, system on chip design, design of solar-powered irrigation systems, optimization of sand mould systems, engine design, optimal sensor deployment and optimal controller design.

Multi-objective optimization has been increasingly employed in chemical engineering and manufacturing.

The problem of Multi-objective optimization through the reconfiguration of a power distribution system, in terms of its definition, is a historical single objective problem with constraints.

Scalarizing a multi-objective optimization problem is an a priori method, which means formulating a single-objective optimization problem such that optimal solutions to the single-objective optimization problem are Pareto optimal solutions to the multi-objective optimization problem.

Example, portfolio Multi-objective optimization is often conducted in terms of mean-variance analysis.

The main advantage of evolutionary algorithms, when applied to solve multi-objective optimization problems, is the fact that they typically generate sets of solutions, allowing computation of an approximation of the entire Pareto front.

Roots for hybrid multi-objective optimization can be traced to the first Dagstuhl seminar organized in November 2004.