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

Yi Chen, Zhonglai Wang, Erfu Yang, Yun Li

Research output: Contribution to conferenceProceeding

4 Citations (Scopus)

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.

Conference

Conference The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016)
Abbreviated titleSKIMA2016
CountryChina
CityChengdu
Period15/12/1617/12/16
Internet address

Fingerprint

Multiobjective optimization
Sorting
Gages
Decision making
Engineers

Keywords

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

Cite this

Chen, Y., Wang, Z., Yang, E., & Li, Y. (2016). Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm. 1-6. The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) , Chengdu, China. https://doi.org/10.1109/SKIMA.2016.7916207
Chen, Yi ; Wang, Zhonglai ; Yang, Erfu ; Li, Yun. / Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm. The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) , Chengdu, China.6 p.
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Chen, Y, Wang, Z, Yang, E & Li, Y 2016, 'Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm' The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) , Chengdu, China, 15/12/16 - 17/12/16, pp. 1-6. https://doi.org/10.1109/SKIMA.2016.7916207

Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm. / Chen, Yi; Wang, Zhonglai; Yang, Erfu; Li, Yun.

2016. 1-6 The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) , Chengdu, China.

Research output: Contribution to conferenceProceeding

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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.

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Chen Y, Wang Z, Yang E, Li Y. Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm. 2016. The 10th International Conference on Software, Knowledge, Information Management and Application (SKIMA 2016) , Chengdu, China. https://doi.org/10.1109/SKIMA.2016.7916207