Multiobjective Evolutionary Optimization
Partha Pratim Biswas, Ponnuthurai Nagaratnam Suganthan · Wiley Encyclopedia of Electrical and Electronics Engineering · 2018
Abstract Many computational techniques have been known for years to solve multiobjective optimization problems ( MOP s). However, the nature of MOP s has been changing and many more large‐scale multimodal MOP s, computationally expensive MOP s, dynamic MOP s, noisy MOP s, 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 make EA an effective tool to apply to complex MOP s. This article provides an overview of multiobjective evolutionary algorithms ( MOEA s), different frameworks of MOEA s, and the application of MOEA s to various MOP s. Performance indicators for MOEA s and some visualization methods in many‐objective optimization problems are also briefly mentioned in this article.