Quality evaluation of solution sets in multiobjective optimisation: a survey

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Quality evaluation of solution sets in multiobjective optimisation : a survey. / Li, Miqing; Yao, Xin.

In: ACM Computing Surveys, Vol. 52, No. 2, 26, 05.2019.

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@article{e5c651efa8904ec8a1d5df8ccbb4d67f,
title = "Quality evaluation of solution sets in multiobjective optimisation: a survey",
abstract = "Complexity and variety of modern multiobjective optimisation problems result in the emergence of numerous search techniques, from traditional mathematical programming to various randomised heuristics. A key issue raised consequently is how to evaluate and compare solution sets generated by these multiobjective search techniques. In this article, we provide a comprehensive review of solution set quality evaluation. Starting with an introduction of basic principles and concepts of set quality evaluation, the paper summarises and categorises 100 state-of-the-art quality indicators, with the focus on what quality aspects these indicators reflect. This is accompanied in each category by detailed descriptions of several representative indicators and in-depth analyses of their strengths and weaknesses. Furthermore, issues regarding attributes that indicators possess and properties that indicators are desirable to have are discussed, in the hope of motivating researchers to look into these important issues when designing quality indicators and of encouraging practitioners to bear these issues in mind when selecting/using quality indicators. Finally, future trends and potential research directions in the area are suggested, together with some guidelines on these directions",
keywords = "Quality evaluation, performance assessment, indicator, metric, measure, multobjective optimisation, multi-criteria optimisation, exact method, heuristic, metaheurisitic, evolutionary algorithms",
author = "Miqing Li and Xin Yao",
year = "2019",
month = may,
doi = "10.1145/3300148",
language = "English",
volume = "52",
journal = "ACM Computing Surveys",
issn = "0360-0300",
publisher = "Association for Computing Machinery ",
number = "2",

}

RIS

TY - JOUR

T1 - Quality evaluation of solution sets in multiobjective optimisation

T2 - a survey

AU - Li, Miqing

AU - Yao, Xin

PY - 2019/5

Y1 - 2019/5

N2 - Complexity and variety of modern multiobjective optimisation problems result in the emergence of numerous search techniques, from traditional mathematical programming to various randomised heuristics. A key issue raised consequently is how to evaluate and compare solution sets generated by these multiobjective search techniques. In this article, we provide a comprehensive review of solution set quality evaluation. Starting with an introduction of basic principles and concepts of set quality evaluation, the paper summarises and categorises 100 state-of-the-art quality indicators, with the focus on what quality aspects these indicators reflect. This is accompanied in each category by detailed descriptions of several representative indicators and in-depth analyses of their strengths and weaknesses. Furthermore, issues regarding attributes that indicators possess and properties that indicators are desirable to have are discussed, in the hope of motivating researchers to look into these important issues when designing quality indicators and of encouraging practitioners to bear these issues in mind when selecting/using quality indicators. Finally, future trends and potential research directions in the area are suggested, together with some guidelines on these directions

AB - Complexity and variety of modern multiobjective optimisation problems result in the emergence of numerous search techniques, from traditional mathematical programming to various randomised heuristics. A key issue raised consequently is how to evaluate and compare solution sets generated by these multiobjective search techniques. In this article, we provide a comprehensive review of solution set quality evaluation. Starting with an introduction of basic principles and concepts of set quality evaluation, the paper summarises and categorises 100 state-of-the-art quality indicators, with the focus on what quality aspects these indicators reflect. This is accompanied in each category by detailed descriptions of several representative indicators and in-depth analyses of their strengths and weaknesses. Furthermore, issues regarding attributes that indicators possess and properties that indicators are desirable to have are discussed, in the hope of motivating researchers to look into these important issues when designing quality indicators and of encouraging practitioners to bear these issues in mind when selecting/using quality indicators. Finally, future trends and potential research directions in the area are suggested, together with some guidelines on these directions

KW - Quality evaluation

KW - performance assessment

KW - indicator

KW - metric

KW - measure

KW - multobjective optimisation

KW - multi-criteria optimisation

KW - exact method

KW - heuristic

KW - metaheurisitic

KW - evolutionary algorithms

U2 - 10.1145/3300148

DO - 10.1145/3300148

M3 - Article

VL - 52

JO - ACM Computing Surveys

JF - ACM Computing Surveys

SN - 0360-0300

IS - 2

M1 - 26

ER -