Paper Info
Title
A tutorial on multiobjective optimization: fundamentals and evolutionary methods.
Abstract
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 population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.
Year
DOI
Venue
2018
10.1007/s11047-018-9685-y
Natural Computing
Keywords
Field
DocType
Multiobjective optimization,Multiobjective evolutionary algorithms,Decomposition-based MOEAs,Indicator-based MOEAs,Pareto-based MOEAs,Performance assessment
Open research,Population,Swarm intelligence,Multi-objective optimization,Artificial intelligence,Multiobjective optimization problem,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
17
3
1567-7818
Citations 
PageRank 
References 
21
0.82
58
Authors
2
Name
Order
Citations
PageRank
Michael T. M. Emmerich124722.74
André H. Deutz218515.50