Multimodal scalarized preferences in multi-objective optimizationCitation formats

Standard

Multimodal scalarized preferences in multi-objective optimization. / Braun, Marlon; Shukla, Pradyumn; Heling, Lars; Schmeck, Hartmut.

GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2017. p. 545-552 (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Braun, M, Shukla, P, Heling, L & Schmeck, H 2017, Multimodal scalarized preferences in multi-objective optimization. in GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, pp. 545-552, 2017 Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, 15/07/17. https://doi.org/10.1145/3071178.3079189

APA

Braun, M., Shukla, P., Heling, L., & Schmeck, H. (2017). Multimodal scalarized preferences in multi-objective optimization. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 545-552). (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3071178.3079189

Vancouver

Braun M, Shukla P, Heling L, Schmeck H. Multimodal scalarized preferences in multi-objective optimization. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. Association for Computing Machinery. 2017. p. 545-552. (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3071178.3079189

Author

Braun, Marlon ; Shukla, Pradyumn ; Heling, Lars ; Schmeck, Hartmut. / Multimodal scalarized preferences in multi-objective optimization. GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2017. pp. 545-552 (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference).

Bibtex

@inproceedings{5df105b3e73d427e8b52649b3eaa6793,
title = "Multimodal scalarized preferences in multi-objective optimization",
abstract = "Scalarization functions represent preferences in multi-objective optimization by mapping the vector of objectives to a single real value. Optimization techniques using scalarized preferences mainly focus on obtaining only a single global preference optimum. Instead, we propose considering all local and global scalarization optima on the global Pareto front. These points represent the best choice in their immediate neighborhood. Additionally, they are usually sufficiently far apart in the objective space to present themselves as true alternatives if the scalarization function cannot capture every detail of the decision maker's true preference. We propose an algorithmic framework for obtaining all scalarization optima of a multi-objective optimization problem. In said framework, an approximation of the global Pareto front is obtained, from which neighborhoods of local optima are identified. Local optimization algorithms are then applied to identify the optimum of every neighborhood. In this way, we have an optima-based approximation of the global Pareto front based on the underlying scalarization function. A computational study reveals that local optimization algorithms must be carefully configured for being able to find all optima.",
keywords = "Evolutionary algorithm, Local optimum, Multi-objective optimization, Multimodal optimization, Scalarization",
author = "Marlon Braun and Pradyumn Shukla and Lars Heling and Hartmut Schmeck",
note = "Publisher Copyright: {\textcopyright} 2017 ACM. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2017 Genetic and Evolutionary Computation Conference, GECCO 2017 ; Conference date: 15-07-2017 Through 19-07-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1145/3071178.3079189",
language = "English",
series = "GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery",
pages = "545--552",
booktitle = "GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference",
address = "United States",

}

RIS

TY - GEN

T1 - Multimodal scalarized preferences in multi-objective optimization

AU - Braun, Marlon

AU - Shukla, Pradyumn

AU - Heling, Lars

AU - Schmeck, Hartmut

N1 - Publisher Copyright: © 2017 ACM. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Scalarization functions represent preferences in multi-objective optimization by mapping the vector of objectives to a single real value. Optimization techniques using scalarized preferences mainly focus on obtaining only a single global preference optimum. Instead, we propose considering all local and global scalarization optima on the global Pareto front. These points represent the best choice in their immediate neighborhood. Additionally, they are usually sufficiently far apart in the objective space to present themselves as true alternatives if the scalarization function cannot capture every detail of the decision maker's true preference. We propose an algorithmic framework for obtaining all scalarization optima of a multi-objective optimization problem. In said framework, an approximation of the global Pareto front is obtained, from which neighborhoods of local optima are identified. Local optimization algorithms are then applied to identify the optimum of every neighborhood. In this way, we have an optima-based approximation of the global Pareto front based on the underlying scalarization function. A computational study reveals that local optimization algorithms must be carefully configured for being able to find all optima.

AB - Scalarization functions represent preferences in multi-objective optimization by mapping the vector of objectives to a single real value. Optimization techniques using scalarized preferences mainly focus on obtaining only a single global preference optimum. Instead, we propose considering all local and global scalarization optima on the global Pareto front. These points represent the best choice in their immediate neighborhood. Additionally, they are usually sufficiently far apart in the objective space to present themselves as true alternatives if the scalarization function cannot capture every detail of the decision maker's true preference. We propose an algorithmic framework for obtaining all scalarization optima of a multi-objective optimization problem. In said framework, an approximation of the global Pareto front is obtained, from which neighborhoods of local optima are identified. Local optimization algorithms are then applied to identify the optimum of every neighborhood. In this way, we have an optima-based approximation of the global Pareto front based on the underlying scalarization function. A computational study reveals that local optimization algorithms must be carefully configured for being able to find all optima.

KW - Evolutionary algorithm

KW - Local optimum

KW - Multi-objective optimization

KW - Multimodal optimization

KW - Scalarization

UR - http://www.scopus.com/inward/record.url?scp=85026418941&partnerID=8YFLogxK

U2 - 10.1145/3071178.3079189

DO - 10.1145/3071178.3079189

M3 - Conference contribution

AN - SCOPUS:85026418941

T3 - GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

SP - 545

EP - 552

BT - GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

PB - Association for Computing Machinery

T2 - 2017 Genetic and Evolutionary Computation Conference, GECCO 2017

Y2 - 15 July 2017 through 19 July 2017

ER -