An Overview of Multi-Objective Evolutionary Algorithms

An Overview of Multi-Objective Evolutionary Algorithms

Evolutionary algorithms (EAs) mimick nature's evolutionary principles to solve search and optimization problems. The basic difference between EAs and classical search and optimization methods is that EAs use a population of solutions, instead of a single solution. Since an iteration of EAs stores and processes multiple solutions, it is convenient to use EAs to find and store multiple optimal solutions in one single simulation run.

This simple fact has motivated a number of evolutionary algorithmists to modify the standard EA procedures to suit to solve multi-objective optimization problems, not only to find solutions close to the Pareto-optimal front but also to maintain a wide diversity among them. This makes the EA approach unique in handling multi-criterion decision making and multi-objective optimization problems