abstract

Local Search Strategies for Multi-Objective Flowshop Scheduling: Introducing Pareto Late Acceptance Hill Climbing

Authors:

Francesca Da Ros,

Luca Di GasperoAuthors Info & Claims

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Pages 61 - 62

https://doi.org/10.1145/3583133.3596428

Published: 24 July 2023 Publication History

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Abstract

We present the Pareto Late Acceptance Hill Climbing algorithm, a multi-objective optimization algorithm based on the Late Acceptance Hill Climbing. We propose an initial experimental analysis of its behavior applying it to different formulations of the bi-objective Permutation Flowshop Scheduling Problem.

References

[1]

Edmund K. Burke and Yuri Bykov. 2017. The late acceptance Hill-Climbing heuristic. European Journal of Operational Research 258, 1 (2017), 70--78.

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[2]

Gerardo Minella, Rubén Ruiz, and Michele Ciavotta. 2008. A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem. INFORMS Journal on Computing 20, 3 (2008), 451--471.

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[3]

Luis Paquete, Marco Chiarandini, and Thomas Stützle. 2004. Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study. In Metaheuristics for multiobjective optimisation. Springer, 177--199.

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Cited By

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Ceschia SDa Ros FDi Gaspero LSchaerf ALi XHandl J(2024)EasyLocal++ a 25-year Perspective on Local Search FrameworksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664140(1658-1667)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664140
Da Ros FDi Gaspero LRoitero KLa Barbera DMizzaro SDella Mea VValent FDeroma L(2024)Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous RegionJournal of Healthcare Informatics Research10.1007/s41666-023-00154-18:2(400-437)Online publication date: 9-Jan-2024
https://doi.org/10.1007/s41666-023-00154-1

Index Terms

Local Search Strategies for Multi-Objective Flowshop Scheduling: Introducing Pareto Late Acceptance Hill Climbing
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
      2. Randomized search
2. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
      1. Combinatorial optimization

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Information

Published In

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

July 2023

2519 pages

ISBN:9798400701207

DOI:10.1145/3583133

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2023

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CINECA

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GECCO '23 Companion

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SIGEVO

GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation

July 15 - 19, 2023

Lisbon, Portugal

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Ceschia SDa Ros FDi Gaspero LSchaerf ALi XHandl J(2024)EasyLocal++ a 25-year Perspective on Local Search FrameworksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664140(1658-1667)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664140
Da Ros FDi Gaspero LRoitero KLa Barbera DMizzaro SDella Mea VValent FDeroma L(2024)Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous RegionJournal of Healthcare Informatics Research10.1007/s41666-023-00154-18:2(400-437)Online publication date: 9-Jan-2024
https://doi.org/10.1007/s41666-023-00154-1

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Recommendations

CLAHC - custom late acceptance hill climbing: first results on TSP

An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times

Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture Search