research-article

A many-objective test problem for visually examining diversity maintenance behavior in a decision space

Authors:

Hisao Ishibuchi,

Yusuke NojimaAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 649 - 656

https://doi.org/10.1145/2001576.2001666

Published: 12 July 2011 Publication History

Abstract

Recently distance minimization problems in a two-dimensional decision space have been utilized as many-objective test problems to visually examine the behavior of evolutionary multi-objective optimization (EMO) algorithms. Such a test problem is usually defined by a single polygon where the distance from a solution to each vertex is minimized in the decision space. We can easily generate different test problems from different polygons. We can also easily generate test problems with multiple equivalent Pareto optimal regions using multiple polygons of the same shape and the same size. Whereas these test problems have a number of advantages, they have no clear relevance to real-world situations since they are artificially generated unrealistic test problems. In this paper, we generate a distance minimization problem from a real-world map. Our test problem has four objectives, which are to minimize the distances to the nearest elementary school, junior high school, railway station, and convenience store. Using our test problem, we examine the behavior of well-known and frequently-used EMO algorithms in terms of their diversity maintenance ability in the two-dimensional decision space.

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Index Terms

A many-objective test problem for visually examining diversity maintenance behavior in a decision space
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

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Published: 12 July 2011

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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https://dl.acm.org/doi/10.5555/3692070.3693683
HE JYANG QXU ZZHANG KZHANG XGE M(2024)Two-stage multimodal multi-objective evolutionary algorithm based on global density updating strategySCIENTIA SINICA Informationis10.1360/SSI-2022-0294Online publication date: 30-Sep-2024
https://doi.org/10.1360/SSI-2022-0294
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https://doi.org/10.1109/TSMC.2023.3325810
Liang JLin HYue CSuganthan PWang Y(2024)Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective OptimizationIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12437711:6(1458-1475)Online publication date: Jun-2024
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