Multiobjective optimization: When objectives exhibit non-uniform latencies

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Multiobjective optimization: When objectives exhibit non-uniform latencies. / Allmendinger, Richard; Handl, Julia; Knowles, Joshua.

In: European Journal of Operational Research, Vol. 243, No. 2, 01.06.2015, p. 497-513.

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@article{85e1a439b2254737858b002639a70050,
title = "Multiobjective optimization: When objectives exhibit non-uniform latencies",
abstract = "Building on recent work by the authors, we consider the problem of performing multiobjective optimization when the objective functions of a problem have differing evaluation times (or latencies). This has general relevance to applications since objective functions do vary greatly in their latency, and there is no reason to expect equal latencies for the objectives in a single problem. To deal with this issue, we provide a general problem definition and suitable notation for describing algorithm schemes that can use different evaluation budgets for each objective. We propose three schemes for the bi-objective version of the problem, including methods that interleave the evaluations of different objectives. All of these can be instantiated with existing multiobjective evolutionary algorithms (MOEAs). In an empirical study we use an indicator-based evolutionary algorithm (IBEA) as the MOEA platform to study performance on several benchmark test functions. Our findings generally show that the default approach of going at the rate of the slow objective is not competitive with our more advanced ones (interleaving evaluations) for most scenarios.",
author = "Richard Allmendinger and Julia Handl and Joshua Knowles",
year = "2015",
month = jun,
day = "1",
doi = "10.1016/j.ejor.2014.09.033",
language = "English",
volume = "243",
pages = "497--513",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Multiobjective optimization: When objectives exhibit non-uniform latencies

AU - Allmendinger, Richard

AU - Handl, Julia

AU - Knowles, Joshua

PY - 2015/6/1

Y1 - 2015/6/1

N2 - Building on recent work by the authors, we consider the problem of performing multiobjective optimization when the objective functions of a problem have differing evaluation times (or latencies). This has general relevance to applications since objective functions do vary greatly in their latency, and there is no reason to expect equal latencies for the objectives in a single problem. To deal with this issue, we provide a general problem definition and suitable notation for describing algorithm schemes that can use different evaluation budgets for each objective. We propose three schemes for the bi-objective version of the problem, including methods that interleave the evaluations of different objectives. All of these can be instantiated with existing multiobjective evolutionary algorithms (MOEAs). In an empirical study we use an indicator-based evolutionary algorithm (IBEA) as the MOEA platform to study performance on several benchmark test functions. Our findings generally show that the default approach of going at the rate of the slow objective is not competitive with our more advanced ones (interleaving evaluations) for most scenarios.

AB - Building on recent work by the authors, we consider the problem of performing multiobjective optimization when the objective functions of a problem have differing evaluation times (or latencies). This has general relevance to applications since objective functions do vary greatly in their latency, and there is no reason to expect equal latencies for the objectives in a single problem. To deal with this issue, we provide a general problem definition and suitable notation for describing algorithm schemes that can use different evaluation budgets for each objective. We propose three schemes for the bi-objective version of the problem, including methods that interleave the evaluations of different objectives. All of these can be instantiated with existing multiobjective evolutionary algorithms (MOEAs). In an empirical study we use an indicator-based evolutionary algorithm (IBEA) as the MOEA platform to study performance on several benchmark test functions. Our findings generally show that the default approach of going at the rate of the slow objective is not competitive with our more advanced ones (interleaving evaluations) for most scenarios.

U2 - 10.1016/j.ejor.2014.09.033

DO - 10.1016/j.ejor.2014.09.033

M3 - Article

VL - 243

SP - 497

EP - 513

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -