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An adaptive sharing elitist evolution strategy for multiobjective optimization.

Lino Costa1, Pedro Oliveira

  • 1Departamento de Produção e Sistemas, Universidade do Minho, Campus de Gualtar, 4710 Braga, Portugal. lac@dps.uminho.pt

Evolutionary Computation
|November 25, 2003
PubMed
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This study introduces the Multiobjective Elitist Evolution Strategy (MEES), a novel approach extending Evolution Strategies (ESs) for multiobjective optimization. MEES demonstrates promising performance compared to existing algorithms.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multiobjective optimization commonly relies on Genetic Algorithms (GAs).
  • Evolution Strategies (ESs) are potent single-objective optimizers but rarely applied to multiobjective problems.
  • Extending ESs to multiobjective optimization is crucial for advancing the field.

Purpose of the Study:

  • To develop and present a novel approach for multiobjective optimization based on Evolution Strategies (ESs).
  • To investigate the efficacy of ESs in handling complex multiobjective optimization tasks.
  • To introduce the Multiobjective Elitist Evolution Strategy (MEES) and evaluate its capabilities.

Main Methods:

  • Development of the Multiobjective Elitist Evolution Strategy (MEES).

Related Experiment Videos

  • Incorporation of performance-enhancing mechanisms, such as elitism, within the ES framework.
  • Comparative analysis of MEES against existing multiobjective optimization algorithms.

    • Main Results:

    • The proposed MEES approach shows significant potential in multiobjective optimization.
    • MEES demonstrates competitive or superior performance compared to other algorithms.
    • Elitism and other incorporated mechanisms contribute to improved performance.

    Conclusions:

    • Evolution Strategies (ESs) can be effectively extended for multiobjective optimization.
    • The Multiobjective Elitist Evolution Strategy (MEES) offers a promising alternative to traditional GA-based methods.
    • Further research into ES-based multiobjective optimization is warranted.