research-article

Multi-objective NK landscapes with heterogeneous objectives

Authors:

Raphaël Cosson,

Roberto Santana,

Arnaud LiefoogheAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 502 - 510

https://doi.org/10.1145/3512290.3528858

Published: 08 July 2022 Publication History

Abstract

So far, multi-objective NK landscapes have been investigated under the assumption of a homogeneous nature of the involved objectives in terms of difficulty. However, we argue that problems with heterogeneous objectives, e.g., in terms of multi-modality, can be challenging for multi-objective evolutionary algorithms, and deserve further considerations. In this paper, we propose a model of multi-objective NK landscapes, where each objective has a different degree of variable interactions (K), as a benchmark to investigate heterogeneous multi-objective optimization problems. We show that the use of a rank-annotated neighborhood network with labeled local optimal solutions, together with landscape metrics extracted from the heterogeneous objectives, thoroughly characterize bi-objective NK landscapes with a different level of heterogeneity among the objectives.

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Cited By

Tanaka SLiefooghe ATakadama KSato H(2024)Designing Helper Objectives in Multi-Objectivization2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612125(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612125
Wang QSang CMa HWang C(2024)A nonrevisiting genetic algorithm based on multi-region guided search strategyComplex & Intelligent Systems10.1007/s40747-024-01627-511:1Online publication date: 12-Nov-2024
https://doi.org/10.1007/s40747-024-01627-5
de Moraes MCoelho G(2024)A benchmark generator for scenario-based discrete optimizationComputational Optimization and Applications10.1007/s10589-024-00551-188:1(349-378)Online publication date: 6-Feb-2024
https://dl.acm.org/doi/10.1007/s10589-024-00551-1

Index Terms

Multi-objective NK landscapes with heterogeneous objectives
1. Applied computing
  1. Operations research
    1. Decision analysis
      1. Multi-criterion optimization and decision-making
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

July 2022

1472 pages

ISBN:9781450392372

DOI:10.1145/3512290

Editor:
Jonathan E. Fieldsend
University of Exeter
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General Chair:
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The University of Adelaide

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Published: 08 July 2022

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July 9 - 13, 2022

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Cited By

Tanaka SLiefooghe ATakadama KSato H(2024)Designing Helper Objectives in Multi-Objectivization2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612125(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612125
Wang QSang CMa HWang C(2024)A nonrevisiting genetic algorithm based on multi-region guided search strategyComplex & Intelligent Systems10.1007/s40747-024-01627-511:1Online publication date: 12-Nov-2024
https://doi.org/10.1007/s40747-024-01627-5
de Moraes MCoelho G(2024)A benchmark generator for scenario-based discrete optimizationComputational Optimization and Applications10.1007/s10589-024-00551-188:1(349-378)Online publication date: 6-Feb-2024
https://dl.acm.org/doi/10.1007/s10589-024-00551-1

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