In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios.
As evolutionary algorithms possess several characteristics that are desirable for this type of …

This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-
objective, multimodal optimization. The Borg MOEA combines-dominance, a measure of …

This paper introduces many objective robust decision making (MORDM). MORDM combines
concepts and methods from many objective evolutionary optimization and robust decision …

This article gives a comprehensive introduction into one of the main branches of
evolutionary computation–the evolution strategies (ES) the history of which dates back to the …

CONTENTS 3.1 Introduction.................................................... 146 3.2 MO
Basics..................................................... 1463.2. 1 Principles of MO............................................ 147 …

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective
evolutionary algorithms (MOEAs) and highlights key advances that the water resources field …

Over the past few years, the research on evolutionary algorithms has demonstrated their
niche in solving multiobjective optimization problems, where the goal is to find a number of …

Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for
solving multiobjective optimization problems. Especially more recent multiobjective …

This article provides a general overview of the field now known as" evolutionary multi-
objective optimization," which refers to the use of evolutionary algorithms to solve problems …