Jove
Visualize
Contact Us

Related Experiment Videos

Multiobjective evolutionary algorithms: analyzing the state-of-the-art.

D A Van Veldhuizen1, G B Lamont

  • 1Air Force Research Laboratory, Optical Radiation Branch, Brooks AFB, TX 78235, USA. david.vanveldhuizen@brooks.af.mil

Evolutionary Computation
|June 8, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Vasectomy in the ram.

The Veterinary record·1968
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

This study reviews multiobjective evolutionary algorithms (MOEAs) for complex optimization. It defines MOEAs, classifies techniques, and evaluates current research for future design recommendations.

Area of Science:

  • Computational intelligence
  • Operations research
  • Computer science

Background:

  • Multiobjective optimization problems (MOPs) present significant challenges due to conflicting objectives.
  • Evolutionary algorithms (EAs) have been adapted since the 1980s to address these complex problems.
  • Multiobjective evolutionary algorithms (MOEAs) have seen widespread application in science and engineering.

Purpose of the Study:

  • To rigorously define MOPs and related concepts.
  • To present a classification scheme for contemporary MOEAs.
  • To critically evaluate current MOEA techniques and theoretical developments.

Main Methods:

  • Review and analysis of existing multiobjective evolutionary algorithm literature.
  • Classification of MOEA techniques based on theoretical and applied aspects.

Related Experiment Videos

  • Evaluation of specific MOEA components: fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations.

    • Main Results:

    • Identification of key analytical insights from current MOEA research and applications.
    • Assessment of the strengths and weaknesses of various contemporary MOEA approaches.
    • Synthesis of findings to inform the design of future MOEAs.

    Conclusions:

    • MOEA development is a rapidly evolving field requiring continuous evaluation.
    • Specific design choices significantly impact MOEA performance in solving MOPs.
    • Recommendations are provided for future research directions and MOEA design principles.