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A tutorial on multiobjective optimization: fundamentals and evolutionary methods

Natural computing, 2018-09, Vol.17 (3), p.585-609 [Peer Reviewed Journal]

The Author(s) 2018 ;Natural Computing is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1567-7818 ;EISSN: 1572-9796 ;DOI: 10.1007/s11047-018-9685-y ;PMID: 30174562

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  • Title:
    A tutorial on multiobjective optimization: fundamentals and evolutionary methods
  • Author: Emmerich, Michael T. M. ; Deutz, André H.
  • Subjects: Artificial Intelligence ; Complex Systems ; Computer Science ; Evolutionary Biology ; Immune system ; Mathematical programming ; Multiple objective analysis ; Optimization ; Pareto optimum ; Performance assessment ; Population (statistical) ; Processor Architectures ; Solvers ; Swarm intelligence ; Theory of Computation
  • Is Part Of: Natural computing, 2018-09, Vol.17 (3), p.585-609
  • Description: 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.
  • Publisher: Dordrecht: Springer Netherlands
  • Language: English
  • Identifier: ISSN: 1567-7818
    EISSN: 1572-9796
    DOI: 10.1007/s11047-018-9685-y
    PMID: 30174562
  • Source: Springer Nature OA/Free Journals
    ProQuest Central
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