Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

Yi Chen; Zhonglai Wang; Erfu Yang; Yun Li

doi:10.1109/SKIMA.2016.7916207

Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

Yi Chen, Zhonglai Wang, Erfu Yang, Yun Li

Design, Manufacturing And Engineering Management

Research output: Contribution to conference › Proceeding › peer-review

8 Citations (Scopus)

279 Downloads (Pure)

Abstract

In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations.

Original language	English
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/SKIMA.2016.7916207
Publication status	Published - 15 Dec 2016
Event	The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) - Sichuan Province, Chengdu, China Duration: 15 Dec 2016 → 17 Dec 2016 http://fusion-edu.eu/SKIMA2016/

Conference

Conference	The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016)
Abbreviated title	SKIMA2016
Country/Territory	China
City	Chengdu
Period	15/12/16 → 17/12/16
Internet address	http://fusion-edu.eu/SKIMA2016/

Keywords

multi-objective optimisation
pareto-optimality
evolutionary algorithm
solution recommendation
artificial wolf-pack algorithm
decision making

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10.1109/SKIMA.2016.7916207

Chen-etal-SKIMA2016-pareto-optimality-solution-recommendation-using-multi-objective-artificialAccepted author manuscript, 1.01 MB

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title = "Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm",

abstract = "In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations.",

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AU - Chen, Yi

AU - Wang, Zhonglai

AU - Yang, Erfu

AU - Li, Yun

N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, f or resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

PY - 2016/12/15

Y1 - 2016/12/15

N2 - In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations.

AB - In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations.

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KW - pareto-optimality

KW - evolutionary algorithm

KW - solution recommendation

KW - artificial wolf-pack algorithm

KW - decision making

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T2 - The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016)

Y2 - 15 December 2016 through 17 December 2016

ER -

Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

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