Multiobjective Evolutionary Optimization

Abstract

Many computational techniques have been known for years to solve multiobjective optimization problems (MOPs). However, the nature ofMOPs has been changing and many more large‐scale multimodalMOPs, computationally expensiveMOPs, dynamicMOPs, noisyMOPs, and so on are introduced in multiobjective optimization domain. The researchers are thus inspired to look beyond the conventional approaches and focus more on evolutionary optimization techniques. The developments in the field of evolutionary algorithm (EA) in last few decades makeEAan effective tool to apply to complexMOPs. This article provides an overview of multiobjective evolutionary algorithms (MOEAs), different frameworks ofMOEAs, and the application ofMOEAs to variousMOPs. Performance indicators forMOEAs and some visualization methods in many‐objective optimization problems are also briefly mentioned in this article.