On the performance metrics of multiobjective optimization

Shi Cheng; Yuhui Shi; Quande Qin

doi:10.1007/978-3-642-30976-2_61

On the performance metrics of multiobjective optimization

Shi Cheng^*
, Yuhui Shi
, Quande Qin

^*Corresponding author for this work

University of Liverpool
Xi'an Jiaotong-Liverpool University
Shenzhen University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

39 Citations (Scopus)

Abstract

Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.

Original language	English
Title of host publication	Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings
Pages	504-512
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-30976-2_61
Publication status	Published - 2012
Event	3rd International Conference on Swarm Intelligence, ICSI 2012 - Shenzhen, China Duration: 17 Jun 2012 → 20 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7331 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Swarm Intelligence, ICSI 2012
Country/Territory	China
City	Shenzhen
Period	17/06/12 → 20/06/12

Keywords

Multiobjective Optimization
Pareto Front/Set
Performance Metrics
Reference Point

Access to Document

10.1007/978-3-642-30976-2_61

Cite this

Cheng, S., Shi, Y., & Qin, Q. (2012). On the performance metrics of multiobjective optimization. In Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings (PART 1 ed., pp. 504-512). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-30976-2_61

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title = "On the performance metrics of multiobjective optimization",

abstract = "Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.",

keywords = "Multiobjective Optimization, Pareto Front/Set, Performance Metrics, Reference Point",

author = "Shi Cheng and Yuhui Shi and Quande Qin",

note = "Funding Information: The authors{\textquoteright} work is partially supported by National Natural Science Foundation of China under grant No.60975080.; 3rd International Conference on Swarm Intelligence, ICSI 2012 ; Conference date: 17-06-2012 Through 20-06-2012",

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Cheng, S, Shi, Y & Qin, Q 2012, On the performance metrics of multiobjective optimization. in Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7331 LNCS, pp. 504-512, 3rd International Conference on Swarm Intelligence, ICSI 2012, Shenzhen, China, 17/06/12. https://doi.org/10.1007/978-3-642-30976-2_61

On the performance metrics of multiobjective optimization. / Cheng, Shi; Shi, Yuhui; Qin, Quande.
Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1. ed. 2012. p. 504-512 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7331 LNCS, No. PART 1).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Shi, Yuhui

AU - Qin, Quande

N1 - Funding Information: The authors’ work is partially supported by National Natural Science Foundation of China under grant No.60975080.

PY - 2012

Y1 - 2012

N2 - Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.

AB - Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.

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KW - Pareto Front/Set

KW - Performance Metrics

KW - Reference Point

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Cheng S, Shi Y, Qin Q. On the performance metrics of multiobjective optimization. In Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings. PART 1 ed. 2012. p. 504-512. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-30976-2_61

On the performance metrics of multiobjective optimization

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